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Applications of functional data analysis: A systematic review

BMC Medical Research Methodology volume 13, Article number: 43 (2013) Cite this article

Abstract

Background

Functional data analysis (FDA) is increasingly being used to better analyze, model and predict time series data. Key aspects of FDA include the choice of smoothing technique, data reduction, adjustment for clustering, functional linear modeling and forecasting methods.

Methods

A systematic review using 11 electronic databases was conducted to identify FDA application studies published in the peer-review literature during 1995–2010. Papers reporting methodological considerations only were excluded, as were non-English articles.

Results

In total, 84 FDA application articles were identified; 75.0% of the reviewed articles have been published since 2005. Application of FDA has appeared in a large number of publications across various fields of sciences; the majority is related to biomedicine applications (21.4%). Overall, 72 studies (85.7%) provided information about the type of smoothing techniques used, with B-spline smoothing (29.8%) being the most popular. Functional principal component analysis (FPCA) for extracting information from functional data was reported in 51 (60.7%) studies. One-quarter (25.0%) of the published studies used functional linear models to describe relationships between explanatory and outcome variables and only 8.3% used FDA for forecasting time series data.

Conclusions

Despite its clear benefits for analyzing time series data, full appreciation of the key features and value of FDA have been limited to date, though the applications show its relevance to many public health and biomedical problems. Wider application of FDA to all studies involving correlated measurements should allow better modeling of, and predictions from, such data in the future especially as FDA makes no a priori age and time effects assumptions.

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Background

Recent increased interest in the application of statistical modeling to medicine, biomedicine, public health, biology, biomechanics and environmental science has largely been driven by the need for good data to inform government policy and planning processes for health service delivery and disease prevention. Importantly, such models will only be useful in the long term if they are accurate, based on good quality data, and generated through application of robust appropriate statistical methods. Functional data analysis (FDA) is one such approach towards modeling time series data that has started to receive attention in the literature, particularly in terms of its public health and biomedical applications.

Commonly, time series data are treated as multivariate data because they are given as a finite discrete time series. This usual multivariate approach completely ignores important information about the smooth functional behavior of the generating process that underpins the data [1]. It also suffers from issues associated with highly correlated measurements within each functional object. The basic idea behind FDA is to express discrete observations arising from time series in the form of a function (to create functional data) that represents the entire measured function as a single observation, and then to draw modeling and/or prediction information from a collection of functional data by applying statistical concepts from multivariate data analysis. In doing so, it has the advantage of generating models that can be described by continuous smooth dynamics, which then allow for accurate estimates of parameters for use in the analysis phase, effective data noise reduction through curve smoothing, and applicability to data with irregular time sampling schedules. Ramsay [2, 3] presents a strong argument for FDA.

Ramsay and Dalzell [4] present several practical reasons for considering functional data:

  1. 1)

    smoothing and interpolation procedures can yield functional representations of a finite set of observations;

  2. 2)

    it is more natural to think through modeling problems in a functional form; and

  3. 3)

    the objectives of an analysis can be functional in nature, as would be the case if finite data were used to estimate an entire function, its derivatives, or the values of other functionals.

Müller has recently described important characteristics of FDA [5]. The FDA approach is highly flexible in the sense that the timing intervals for data observations do not have to be equally spaced for all cases and can vary across cases. Importantly, FDA methods are not necessarily based on the assumption that the values observed at different times for a single subject are independent. Although functional data themselves are not new, a new conceptualization of them has become necessary because of the increasing sophistication of available data collections [4]. Data collection technology has evolved over recent decades, allowing more dense sampling of observations over time, space, and other continuum measures. Such data are usually interpreted as reflecting the influence of certain smooth functions that are assumed to underlie and to generate the observations. Although classical multivariate statistical techniques are often applied to such data, they do not take advantage of additional information that could be implied by the smoothness of underlying functions. In particular, FDA methods can often extract additional information contained in the function and its derivatives [6, 7] that is not normally available from application of traditional statistical methods [1]. Because the FDA approach essentially treats the whole curve as a single entity, there is also no concern about correlations between repeated measurements. This represents a change in philosophy towards the handling of time series and correlated data [8].

There are a number of good illustrations of applications of FDA; for example, Ramsay and Silverman [9, 10] using curves as data, Locantore et al. [11] with images as data, and Yushkevich and Pizer [12] where the data points are shape representations of body parts. Application of FDA has also been published across various scientific fields including analysis of child size evolution [9], climatic variation [4, 13], handwriting in Chinese [14], acidification processes [15], land usage prediction based on satellite images [16], medical research [17–19], behavioral sciences [20], term-structured yield curves [21], and spectrometry data [22]. Most recently, Ullah and Finch [23] found FDA to be an effective exploratory and modeling technique for highlighting trends and variations in the shape of the age–falls injury incidence relationship over time.

In contrast to most other methods commonly used to model trends in time series data, a key strength of the FDA approach is that it makes no parametric assumptions about age or time effects. The FDA methods for modeling and forecasting data across a range of health and demographic issues also have significant advantages for better understanding trends, risk factor relationships, and the effectiveness of preventive measures [24, 25]. In the book Functional Data Analysis, Ramsay and Silverman [9] give an accessible overview of the foundations and applications of FDA. In an earlier book entitled Applied Functional Data Analysis, the same authors [10] provide many examples that share the property of being functional forms of a continuous variable, most often age or time. In 2004, Statistica Sinica published a special issue that included two relevant review articles that dealt exclusively with the close connection between longitudinal data and functional data [26, 27]. In his PhD thesis, Ullah [28] described the significance and application of FDA in demographic data settings. Software developed for MATLAB, S-PLUS and R by Ramsay and Silverman specifically to support FDA is available from <http://www.psych.mcgill.ca/misc/fda/>.

Because the application of FDA is still relatively novel, especially to public health and biomedical data, this paper reviews applications of the approach to date with the aim of encouraging researchers to adopt FDA in future studies. This paper begins with a systematic review of the focus and application features of published peer-reviewed FDA studies. In doing so, it provides a summary of the extent to which FDA has been applied in different fields, including an overview of the nature of the time series variables/data used. For each of the identified studies, this paper also describes the features of FDA that were used, including the:

  1. (1)

    representation of data via principal components analysis, which plays a key role in defining smoothness and continuity conditions of the resulting data;

  2. (2)

    classification of data, which produces different functional groups (or clusters) for gaining more sophisticated knowledge of different pathways and/or functions for large scale data;

  3. (3)

    functional linear models used for testing the effects on outcomes in functional form; and

  4. (4)

    forecasting via stochastic methods, to measure the forecast uncertainty through the estimation of a prediction interval.

Methods

This review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) Statement [29]. We conducted a systematic search of 11 electronic databases to identify peer-reviewed FDA application studies published between January 1995 and December 2010. The databases used were Academic Search Premier, ScienceDirect, SpringerLink, Cambridge Journals, MEDGE (Informit), Oxford Journals, PubMed, Sage Journals Online, Web of Science, Wiley Interscience Journals, and MEDLINE. We used the phrase functional data analysis to identify relevant articles, and considered only English language articles published in peer-reviewed journals. In addition to the electronic database search, the search strategy included secondary searching of the reference lists of identified articles.

Inclusion and exclusion criteria

Studies were eligible for inclusion if they were original research articles in peer-reviewed journals reporting an application of FDA. We excluded studies of statistical methodology development without application, and abstracts, letters, and conference papers.

Identification of studies

The first author, with the assistance of two research assistants, sourced and screened all identified articles. This included viewing titles and reading abstracts. We obtained full text versions of potentially eligible articles, assessed them against the exclusion/inclusion criteria, and removed obvious exclusions.

In the first review phase, 334 articles were identified. Figure 1 summarizes the numbers of studies identified and the reasons for exclusion at each stage. Searching the titles and abstracts of identified studies excluded 160 (47.9%) articles that were not directly relevant to statistical FDA applications. These included reports of functional magnetic resonance imaging (fMRI) to assess patterns of brain activation in patients suffering from chronic traumatic brain injury [30], functional performance in participants with functional ankle instability [31], and the relationship between neurocognitive function and noncontact anterior cruciate ligament injuries [32].

Figure 1
figure 1

Systematic search strategy used to identify 84 peer-review studies with published application of functional data analysis (FDA).

Full size image

In the second phase, we conducted a complete detailed review of the remaining 174 retrieved articles to ensure they fully met the inclusion and exclusion criteria. A further 102 articles were excluded at this stage, leaving 72 peer reviewed articles for the third phase. Studies excluded at this stage were mainly those that justified FDA theory rather than presenting examples of its application [8, 33–35]. A further 12 articles were found in the manual search of reference lists of the 72 retained articles.

We retained a final set of 84 articles for detailed review. The lead author reviewed each paper in terms of key FDA criteria, as outlined below, and assessed its field of application and the specific FDA methods applied. Figure 1 uses the PRISMA [29] flowchart to summarize all stages of the paper selection process.

Results

Overview of the published FDA studies

Table 1 summarizes the final set of reviewed papers, and shows fields of application, outcome of interest, and use of the following important FDA features:

  •  smoothing technique;

  •  use of functional principal component analysis (FPCA);

  •  type of clustering adjustment;

  •  functional linear modeling (FLM) approach adopted to relate explanatory and outcome variables; and

  •  type of forecasting (if any).

Table 1 Areas of application and the functional data analysis (FDA) features used in the 84 peer-review papers reporting application of FDA
Full size table

The earliest identified application of FDA was in 1995 and 75% of the reviewed articles were published since 2005. This reflects increasing recognition of the important features of functional data and awareness of the development of new statistical approaches and software for handling them.

While diverse fields were covered in the published studies, almost 21% of the studies related specifically to biomedical science (18 identified papers), followed by biomechanics applications (11 papers). Other fields of application were medicine (10), linguistics (6), biology (4), ecology (4), psychology (4), meteorology (4), environmental studies (4), demography (3), finance (3), neurology (2), economics (2), engineering (2), agriculture (1), physiology (1), information technology (1), education (1), chemistry (1), geophysics (1), and behavioral science (1). In relation to specific health issues, the most common topics were analyses of kinematics gait data (9 papers), magnetic resonance imaging (6 papers), and yeast cell cycle temporal gene expression profiles (6 papers).

Important features of the published FDA studies

Table 1 summarizes the published studies in terms of their use of the following key features of FDA: the reported smoothing technique, FPCA, clustering, the adopted forms of the FLM and forecasting. The importance of each of these features is explained below and an overview given of how they were handled in the published studies.

Smoothing techniques

Smoothing is the first step in any FDA, and its purpose is to convert raw discrete data points into a smoothly varying function. This emphasizes patterns in the data by minimizing short-term deviations due to observational errors, such as measurement errors or inherent system noise. When reporting FDA studies, it is important to state the smoothing approach used because observational errors always exist in longitudinal data.

Table 1 summarizes the various smoothing techniques used to estimate functions from the discrete observations reported in the reviewed literature. Overall, all except twelve of the studies (i.e. 85.7% of the reviewed studies) provided information about the type of smoothing technique used. Although some authors believe that FDA can be considered as a smoothed version of multivariate data analysis, smoothing techniques should still be used to reduce some of the inherent randomness in the observed data [1, 25, 113].

Overall, B-spline smoothing was the most popular smoothing technique used (25 papers), presumably because of its simplicity and flexibility for tackling a wide range of nonparametric and semiparametric modeling situations. A common approach towards B-spline smoothing is to construct a large number of knots (as the smoothing parameter) to reduce the effective degrees of freedom and increase smoothness in the overall function estimate [114, 115]. Other smoothing techniques adopted in the published studies included use of Fourier smoothing (8 papers), regression splines (6), kernel smoothing (7), polynomial splines (5), cubic splines (3), smoothing splines (3), wavelet bases (3), roughness penalties (2), local polynomials (2), local quadratics (1), local weighted regression (1), P-splines (1) and log-splines (1).

Ramsay and Silverman [9] emphasize that the choice of smoothing technique is dependent upon the underlying behavior of the data being analyzed. Ideally, the smoother should reflect or have features that match those of the data. For example, Fourier smoothers are traditionally used when the data are cyclical or periodic. Environmental diurnal ozone and NOx cycles [71, 116], trends in ecologically meaningful water quality variates in ecology [81], cash flows in finance [92] and fetal heart rate monitoring in medicine [18, 19] are examples of the application of Fourier smoothers. Splines (regression splines, polynomial splines, B-spline) are typically chosen to represent noncyclical nonperiodic data [25, 51, 84], and wavelet bases are chosen to represent data displaying discontinuities and/or rapid changes in behavior [117, 118]. Most recently, Ullah and Finch [23] used constrained penalized regression splines with a monotonic constraint to represent their smooth curves of falls incidence rates.

Data reduction

The FPCA is one of the most popular multivariate analysis techniques for extracting information from functional data [119, 120]. This approach reduces the dimensions of a data set in which there are a large number of interrelated variables, while still holding as much of the total variation as possible. This reduction is obtained by transforming the data to a new set of variables, or principal components, that are uncorrelated and ordered so that the first few retain most of the variation present in all of the original variables.

The use of FPCA was reported in 51 (60.7%) of the reviewed studies (Table 1). It has been successfully applied to real life problems such as modeling the curvature of the cornea in the human eye [11], in a set of density curves where the argument variable is log income [121], and fMRI scans of areas in the human brain [88]. Many different applications of principal component analysis to functional data have been developed, including a useful extension of FPCA that allows the estimation of harmonics from fragments of curves [122]. Although FPCA is an important feature of FDA, not all studies reported it because they did not undertake data reduction. For example, Roislin et al. [48] used a functional regression model to estimate the effects of gender, age, and walking speed on kinematic gait data; Park et al. [58] classified gene functions using a support vector machine (clustering) for time-course gene expression data; and Lucero [93] used only a B-spline to smooth the harmonics-to-noise ratio of voice signals. None of these applications required FPCA to reduce the data.

Clustering

While FPCA results in dimension reduction, FPCA vector scores can be used for clustering different functions/components using standard clustering methods. Clustering is one of the most frequently used techniques for partitioning a dataset into subgroups that contain instances that are similar to each other while being clearly dissimilar to those of other groups. In a functional context, clustering helps to identify representative curve patterns and individuals who are very likely to be involved in the same or similar processes. For example, in time-course microarray experiments, thousands of gene expression measures are taken over time [123] and an important problem is to discover functionally related genes that could then be the target for new gene regulatory networks or functional pathways. Clustering of data allows identification of groups of genes with similar expression patterns to identify such networks and pathways.

A number of clustering methods were reported in the reviewed literature (Table 1) and most of these were exploratory techniques for gene expression data. Overall, 15 studies (17.9%) reported some form of clustering. Biologists and ecologists used clustering to classify genes [68, 76] and ecological components [81, 90] within their studies. The most commonly applied clustering algorithms were hierarchical in nature (7 papers). Hierarchical algorithms define a dendrogram (tree) relating similar objects in the same sub-trees. In each step, similar sub-trees (clusters) are merged to form a dendrogram that clearly shows the different distinct clusters. Other reported clustering methods were linear discriminant analysis (LDA) (2 papers), k-nearest neighbors (KNN) (3), support vector machine (SVM) approaches (2), model-based clustering (MBC) (1), quadratic discriminant analysis (QDA) (1) and estimated differential operators (EDO) (1).

The LDA and QDA are both classical clustering methods and commonly used in microarray analysis [124, 125]. Application of LDA is based on finding linear combinations of gene expression levels called discriminants that maximize the ratio of between-group variation to within-group variation. The QDA approach is a generalization of the linear classifier, allowing covariance matrices to be heterogeneous, whereas LDA functions are based on the assumption that covariance matrices of each of the classes are equal. This assumption relaxation can prevent individuals from being placed into classes with larger variance on their covariance matrix diagonals. The KNN is a nonparametric classification method based on the distance between individuals [126, 127]. For example, Song et al. [59] proposed KNN to classify time-course gene expression profiles based on information from the data patterns. The SVM approach [128] is an extremely powerful methodology for classification problems and has a wide range of applications. Recently, this method has received much attention in classification problems that arise with the analysis of microarray data [58, 59]. The MBC method assumes that the data are generated by a multivariate normal mixture distribution with appropriate means and covariance matrix [129]. Song et al. [68] have applied this method of clustering time-course gene expression data.

Functional linear models

An interesting application of FDA involves the construction of functional models that describe the relation between an outcome variable and an explanatory variable. Such models are termed functional linear models (FLMs). The number of published applications involving functional data has been steadily growing. In functional linear models, the functions could be the outcome or the predictors or both.

Of the reviewed studies in Table 1, 21 (25.0%) reported some form of FLM. The approach most favored by authors was a basic functional linear regression model (12 papers). When the outcome variable is in its functional form and the relationship is almost linear, the methodology is called functional linear regression model, or FRM. Functional ANOVA (FANOVA) was used in eight studies. Vines et al. [85] developed a functional F test (FFT) for linear models with functional outcomes in their psychological study for measuring tension judgment in music. They illustrated how to apply the FFT to longitudinal data where intrasubject repeated measures are viewed as discrete samples from an underlying curve with a continuous functional form. One study applied a functional logistic regression model (FLRM) to fetal heart rates [18] and another applied functional multivariate analysis of variance (FMANOVA) to temporal fertility trajectories of medfly populations [47].

Forecasting framework

The recent introduction of stochastic methods for forecasting functional data has significant advantages over the standard approaches for better understanding trends, risk factor relationships, and the effectiveness of preventive measures. A major advantage of these methods is that they can measure forecast uncertainty through the estimation of prediction intervals for future data. For this reason, the FDA forecasting approach has started to receive attention in both demographic and medical applications [24, 25, 28, 60]. To date there has only been limited application of FDA to epidemiological studies relating to the prediction of incidence/prevalence rates, with only one recent study applying it to forecast the incidence of fall-related severe head injuries [23].

Overall, only seven of the reviewed studies (8.3%) reported any FDA-derived forecasting. A state space model was the most common approach for forecasting functional data in these studies (5 papers). In the forecasting process, the authors estimated the coefficients from a time series, with one value representing each time point, and a state space model was used to model and forecast these time series coefficients [23–25, 28, 130].

Discussion

Modern data analysis has greatly benefited from the development of FDA methods and their application to time series data. Although used by statisticians for many years, FDA provides a relatively novel approach to modeling and prediction that is highly suitable for public health and biomedical applications. This paper has summarized papers describing FDA applications with a main emphasis on five popular features: smoothing, FPCA, clustering, FLM, and forecasting.

Overall, the published FDA application studies demonstrate the value of this approach for exploring complex multivariate functional relationships and its major strength of being able to model the functional form of time series data. Different approaches allow for FDA representations as smooth functions, and the published studies used a range of smoothing techniques for the estimation of discretely observed data. The FDA approach of initially smoothing the data and then using the smoothed observations for modeling and forecasting is a major methodological improvement over methods that simply fit linear/non-linear trends to observed data. These FDA approaches are very suitable for widespread public health and biomedical applications. Although some authors believe that FDA can be considered as a smoothed version of multivariate data analysis, recent work has shown the advantage of direct application of smoothing techniques to reduce some of the inherent randomness in the observed data [1, 25, 113].

The theoretical and practical developments that have occurred over recent years mean that researchers can now successfully apply FPCA to many practical problems, with main attention given to the reduction of data dimensions to a finite level and identification of the most significant components of the data. High dimensional data significantly slow down conventional statistical algorithms and in some cases it is not feasible to use them in practice. This means that standard classification methods can suffer from difficulties when handling such data. Some studies need to compress their data to facilitate exploration of the most important features (e.g., characteristics of genes from entire time-course data). In such instances, dimension reduction should be applied to keep only the relevant information and for removing correlations. This will both speed up and improve the accuracy of subsequent analyses and modeling. The FPCA has proven to be a key technique for dimension reduction, reported in most of studies reviewed here. It can also be used to investigate the variability of data with respect to individual curve shapes [131].

One of the major application areas highlighted in this review is an apparent increasing interest in clustering and classification techniques, especially for time-course gene expression data. The clustering is useful for detecting patterns and clusters in high dimensional functional data. Functional clustering is used to search for natural groupings of data with similar characteristics. Unlike conventional clustering that requires measuring multivariate data at the same time points to calculate Euclidean ‘distances’ between observations, functional clustering can derive a broader class of distance measures even if the original measurements are not time-aligned among sampling units, as is common in public health applications. The reason for the popularity of functional clustering is that it can classify time series data into different classes without requiring a priori knowledge of data.

A very interesting application of FDA involves the construction of linear models that describe the relation between an outcome variable and explanatory variables with functional nature. The FLMs have recently gained popularity and the related literature has been steadily growing with several studies using covariates to explain functional variables. Overall, FRM and FANOVA methods were the most prominent in the reviewed literature. Reasons for not using FLM techniques are unclear but might include a lack of knowledge about the value of building functional models for public health and biomedical data. However, the use of FLM is not always necessary and depends on the specific research questions.

Public health researchers now recognize the importance of understanding trends in high dimensional time series data. Policy makers, for example, need information about predicted trends to inform their decision-making about public health and economic investments to reduce the burden into the future [132]. It is critical that such predictions are robust and based on the best available statistical modeling approaches to minimize possible errors in the forecasts. This is also true for other areas of public health and biomedicine. The new FDA forecasting approaches [23, 25] are a natural extension of methods developed for mortality and fertility forecasting that have evolved over the last two decades in demography [25, 133, 134]. The methodology has therefore been used in a number of demographic applications and there have been various extensions and modifications proposed [25, 134]. Somewhat surprisingly, the use of FDA forecasting in public health and biomedical applications has been limited to date.

Conclusion

In summary, this paper describes FDA and its important features as applied to time series data from various fields. Functional data analysis provides a relatively novel modeling and prediction approach, with the potential for many significant applications across a range of public health and biomedical applications. Importantly, not all FDA features always need to be used in a single study and the selection of specific analysis features will depend on the underlying behavior of the data, the nature of study and the specific research questions being posed. Consideration should be given to wider application of FDA and its important modeling features so that more accurate estimates for public health and biomedical applications can be generated.

Abbreviations

FDA:

Functional data analysis

FPCA:

Functional principal component analysis

FLM:

Functional linear modeling

LDA:

Linear discriminant analysis

KNN:

K-nearest neighbours

SVM:

Support vector machine

MBC:

Model based clustering

QDA:

Quadratic discriminant analysis

EDO:

Estimated differential operators

FRM:

Functional linear regression model

FANOVA:

Functional ANOVA

FFT:

Functional F-test

FLRM:

Functional logistic regression model

FMANOVA:

Functional multivariate analysis of variance.

References

  1. Green PJ, Silverman BW: Nonparametric regression and generalized linear models: A roughness penalty approach. 1994, London: Chapman and Hall

    Google Scholar 

  2. Ramsay JO: When the data are functions?. Psychometrika. 1982, 47: 379-396. 10.1007/BF02293704.

    Google Scholar 

  3. Ramsay JO: Monotone regression splines in action. Statist Sci. 1988, 3: 425-441. 10.1214/ss/1177012761.

    Google Scholar 

  4. Ramsay JO, Dalzell CJ: Some tools for functional data analysis. J R Stat Soc Series B Stat Methodol. 1991, 53: 539-572.

    Google Scholar 

  5. Müller HG: Functional data analysis. 2011, StatProb: The Encyclopedia Sponsored by Statistics and Probability Societies

    Google Scholar 

  6. Ferraty F, Mas A, Vieu P: Advances in nonparametric regression for functional variables. Aust N Z J Stat. 2007, 49: 1-20.

    Google Scholar 

  7. Mas A, Pumo B: Functional linear regression with derivatives. J Nonparametr Stat. 2009, 21: 19-40. 10.1080/10485250802401046.

    Google Scholar 

  8. Levitin DJ, Nuzzo RL, Vines BW, Ramsay JO: Introduction to functional data analysis. Can Psychol. 2007, 48: 135-155.

    Google Scholar 

  9. Ramsay JO, Silverman BW: Functional data analysis. 2005, New York: Springer, 2

    Google Scholar 

  10. Ramsay JO, Silverman BW: Applied functional data analysis. 2002, New York: Springer,

    Google Scholar 

  11. Locantore N, Marron JS, Simpson DG, Tripoli N, Zhang JT, Kohen KL: Robust principal component analysis for functional data. TEST. 1999, 8: 1-73. 10.1007/BF02595862.

    Google Scholar 

  12. Yushkevich P, Pizer SM, Joshi S, Maron JS: Proceedings of the Inf Process Med Imaging. Intuitive, localized analysis of shape variability. 2001, 2082/2001, 402-408.

    Google Scholar 

  13. Besse PC, Cardot H: Autoregressive forecasting of some functional climate variations. Scand Stat Theory Appl. 2000, 27: 673-687.

    Google Scholar 

  14. Ramsay JO: Functional components of variation in handwriting. J Am Stat Assoc. 2000, 95: 9-15. 10.1080/01621459.2000.10473894.

    Google Scholar 

  15. Abraham C, Cornillon PA, MatznerLober E, Molinari N: Unsupervised curve clustering using B-splines. Scand Stat Theory Appl. 2003, 30: 581-595.

    Google Scholar 

  16. Besse PC, Cardot H, Faivre R, Goulard M: Statistical modelling of functional data. Appl Stoch Model Bus Ind. 2005, 21: 165-173. 10.1002/asmb.539.

    Google Scholar 

  17. Pfeiffer RM, Bura E, Smith A, Rutter JL: Two approaches to mutation detection based on functional data. Stat Med. 2002, 21: 3447-3464. 10.1002/sim.1269.

    PubMed  Google Scholar 

  18. Ratcliffe SJ, Heller GZ, Leader LR: Functional data analysis with application to periodically stimulated foetal heart rate data. II: functional logistic regression. Stat Med. 2002, 21: 1115-1127. 10.1002/sim.1068.

    PubMed  Google Scholar 

  19. Ratcliffe SJ, Leader LR, Heller GZ: Functional data analysis with application to periodically stimulated foetal heart rate data. I: functional regression. Stat Med. 2002, 21: 1103-1114. 10.1002/sim.1067.

    PubMed  Google Scholar 

  20. Rossi N, Wang X, Ramsay JO: Nonparametric item response function estimates with the EM algorithm. J Educ Behav Stat. 2002, 27: 291-317. 10.3102/10769986027003291.

    Google Scholar 

  21. Kargin V, Onatski A: Curve forecasting by functional autoregression. J Multivar Anal. 2008, 99: 2508-2526. 10.1016/j.jmva.2008.03.001.

    Google Scholar 

  22. Reiss PT, Ogden RT: Functional principal component regression and functional partial least squares. J Am Stat Assoc. 2007, 102: 984-996. 10.1198/016214507000000527.

    CAS  Google Scholar 

  23. Ullah S, Finch CF: Functional data modelling approach for analysing and predicting trends in incidence rates-An application to falls injury. Osteoporos Int. 2010, 21: 2125-2134. 10.1007/s00198-010-1189-2.

    CAS  PubMed  Google Scholar 

  24. Erbas B, Hyndman RJ, Gertig DM: Forecasting age-specific breast cancer mortality using functional data models. Stat Med. 2007, 26: 458-470. 10.1002/sim.2306.

    PubMed  Google Scholar 

  25. Hyndman RJ, Ullah MS: Robust forecasting of mortality and fertility rates: a functional data approach. Comput Stat Data Anal. 2007, 51: 4942-4956. 10.1016/j.csda.2006.07.028.

    Google Scholar 

  26. Rice JA: Functional and longitudinal data analysis: perspectives on smoothing. Statist Sci. 2004, 14: 631-647.

    Google Scholar 

  27. Davidian M, Lin X, Wang JL: Introduction: emerging issues in longitudinal and functional data analysis. Statist Sci. 2004, 14: 613-614.

    Google Scholar 

  28. Ullah S: Demographic forecasting using functional data analysis. 2007, Monash University: PhD Thesis

    Google Scholar 

  29. Moher D, Liberati A, Tetzlaff J, Altman DG, The PRISMA Group: Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6: e1000097-10.1371/journal.pmed.1000097.

    PubMed  PubMed Central  Google Scholar 

  30. Rasmussen IJ, Antonsen IK, Berntsen EM, Xu J, Lagopoulos J, Haberg AK: Brain activation measured using functional magnetic resonance imaging during the Tower of London task. Acta Neuropsychiatr. 2006, 18: 216-225. 10.1111/j.1601-5215.2006.00145.x.

    PubMed  Google Scholar 

  31. Buchanan AS, Docherty CL, Schrader J: Functional performance testing in participants with functional ankle instability and in a healthy control group. J Athl Train. 2008, 43: 342-346. 10.4085/1062-6050-43.4.342.

    PubMed  PubMed Central  Google Scholar 

  32. Swanik CB, Covassin T, Stearne DJ, Schatz P: The relationship between neurocognitive function and noncontact anterior cruciate ligament injuries. Am J Sports Med. 2007, 35: 943-948. 10.1177/0363546507299532.

    PubMed  Google Scholar 

  33. Gabrys R, Kokoszka P: Portmanteau test of independence for functional observations. J Am Stat Assoc. 2007, 102: 1338-1348. 10.1198/016214507000001111.

    CAS  Google Scholar 

  34. Manteiga WG, Vieu P: Statistics for functional data. Comput Stat Data Anal. 2007, 51: 4788-4792. 10.1016/j.csda.2006.10.017.

    Google Scholar 

  35. Cardot H, Ferraty F, Mas A, Sarda P: Testing hypotheses in the functional linear model. Scand Stat Theory Appl. 2003, 30: 241-255.

    Google Scholar 

  36. Dura JV, Belda JM, Poveda R, Page A, Laparra J, Das J, Prat J, Garcia AC: Comparison of functional regression and non-functional regression approaches to the study of the walking velocity effect in force platform measures. J Appl Biomech. 2010, 26: 234-239.

    PubMed  Google Scholar 

  37. Crane EA, Cassidy RB, Rothman ED, Gerstner GE: Effect of registration on cyclical kinematic data. J Biomech. 2010, 43: 2444-2447. 10.1016/j.jbiomech.2010.04.024.

    PubMed  PubMed Central  Google Scholar 

  38. Zhu HT, Styner M, Tang NS, Liu ZX, Lin WL, Gilmore JH: FRATS: Functional regression analysis of DTI tract statistics. IEEE Trans Med Imaging. 2010, 29: 1039-1049.

    PubMed  PubMed Central  Google Scholar 

  39. Wu P, Müller H: Functional embedding for the classification of gene expression profiles. Bioinformatics. 2010, 26: 509-517. 10.1093/bioinformatics/btp711.

    PubMed  Google Scholar 

  40. Kim SB, Rattakorn P, Peng YB: An effective clustering procedure of neuronal response profiles in graded thermal stimulation. Expert Syst Appl. 2010, 37: 5818-5826. 10.1016/j.eswa.2010.02.025.

    Google Scholar 

  41. Hyndman RJ, Shang HL: Rainbow plots, bagplots, and boxplots for functional Data. J Comput Graph Stat. 2010, 19: 29-45. 10.1198/jcgs.2009.08158.

    Google Scholar 

  42. Torres JM, Garcia Nieto PJ, Alejano L, Reyes AN: Detection of outliers in gas emissions from urban areas using functional data analysis. J Hazard Mater. 2010, 186: 144-149.

    PubMed  Google Scholar 

  43. Maslova I, Kokoszka P, Sojka J, Zhu L: Statistical significance testing for the association of magnetometer records at high-, mid- and low latitudes during substorm days. Planet Space Sci. 2010, 58: 437-445. 10.1016/j.pss.2009.11.004.

    Google Scholar 

  44. Hermanussen M: Auxology: An update. Horm Res Paediatr. 2010, 74: 153-164. 10.1159/000317440.

    CAS  PubMed  Google Scholar 

  45. Erbas B, Akram M, Gertig DM, English D, Hopper JL, Kavanagh AM, Hyndman RJ: Using functional data analysis models to estimate future time trends in age-specific breast cancer mortality for the United States and England-Wales. J Epidemiol. 2010, 20: 159-165. 10.2188/jea.JE20090072.

    PubMed  PubMed Central  Google Scholar 

  46. Ogden RT, Greene E: Wavelet modeling of functional random effects with application to human vision data. J Stat Plan Inference. 2010, 140: 3797-3808. 10.1016/j.jspi.2010.04.044.

    Google Scholar 

  47. Muller HG, Wu S, Diamantidis AD, Papadopoulos NT, Carey JR: Reproduction is adapted to survival characteristics across geographically isolated medfly populations. Proc Biol Sci. 2009, 276: 4409-4416. 10.1098/rspb.2009.1461.

    PubMed  PubMed Central  Google Scholar 

  48. Roislien J, Skare O, Gustavsen M, Broch NL, Rennie L, Opheim A: Simultaneous estimation of effects of gender, age and walking speed on kinematic gait data. Gait Posture. 2009, 30: 441-445. 10.1016/j.gaitpost.2009.07.002.

    PubMed  Google Scholar 

  49. Gouttard S, Prastawa M, Bullitt E, Lin W, Gerig G: Constrained data decomposition and regression for analyzing healthy aging from fiber tract diffusion properties. Med Image Comput Comput Assist Interv. 2009, 12: 321-328.

    PubMed  PubMed Central  Google Scholar 

  50. Illian JB, Prosser JI, Baker KL, Rangel-Castro JL: Functional principal component data analysis: a new method for analysing microbial community fingerprints. J Microbiol Methods. 2009, 79: 89-95. 10.1016/j.mimet.2009.08.010.

    CAS  PubMed  Google Scholar 

  51. Parker BJ, Wen J: Predicting microRNA targets in time-series microarray experiments via functional data analysis. BMC Bioinforma. 2009, 10: S32-10.1186/1471-2105-10-S1-S32.

    Google Scholar 

  52. Goodlett CB, Fletcher PT, Gilmore GH, Gerig G: Group statistics of DTI fiber bundles using spatial functions of tensor measures. Med Image Comput Comput Assist Interv. 2008, 11: 1068-1075.

    PubMed  PubMed Central  Google Scholar 

  53. Jiang CR, Aston JA, Wang JL: Smoothing dynamic positron emission tomography time courses using functional principal components. NeuroImage. 2009, 47: 184-193. 10.1016/j.neuroimage.2009.03.051.

    PubMed  PubMed Central  Google Scholar 

  54. Lee KL, Meyer RJ, Bradlow ET: Analyzing risk response dynamics on the web: the case of Hurricane Katrina. Risk Anal. 2009, 29: 1779-1792. 10.1111/j.1539-6924.2009.01304.x.

    PubMed  Google Scholar 

  55. Oa D, Harrison AJ, Coffey N, Hayes K: Functional data analysis of running kinematics in chronic Achilles tendon injury. Med Sci Sports Exerc. 2008, 40: 1323-1335. 10.1249/MSS.0b013e31816c4807.

    Google Scholar 

  56. Baladandayuthapani V, Mallick BK, Young HM, Lupton JR, Turner ND, Carroll RJ: Bayesian hierarchical spatially correlated functional data analysis with application to colon carcinogenesis. Biometrics. 2008, 64: 64-73. 10.1111/j.1541-0420.2007.00846.x.

    PubMed  Google Scholar 

  57. Muller H, Chiou J, Leng X: Inferring gene expression dynamics via functional regression analysis. BMC Bioinforma. 2008, 9: 60-10.1186/1471-2105-9-60.

    Google Scholar 

  58. Park C, Koo J, Kim S, Sohn I, Lee JW: Classification of gene functions using support vector machine for time-course gene expression data. Comput Stat Data Anal. 2008, 52: 2578-2587. 10.1016/j.csda.2007.09.002.

    Google Scholar 

  59. Song JJ, Deng W, Lee H, Kwon D: Optimal classification for time-course gene expression data using functional data analysis. Comp Bio Chem. 2008, 32 (6): 426-432. 10.1016/j.compbiolchem.2008.07.007.

    CAS  Google Scholar 

  60. Hyndman R, Booth H: Stochastic population forecasts using functional data models for mortality, fertility and migration. Int J Forecast. 2008, 24: 323-342. 10.1016/j.ijforecast.2008.02.009.

    Google Scholar 

  61. Ikeda T, Dowd M, Martin JL: Application of functional data analysis to investigate seasonal progression with interannual variability in plankton abundance in the bay of Fundy, Canada. Estuar Coast Shelf Sci. 2008, 78: 445-455. 10.1016/j.ecss.2007.12.011.

    Google Scholar 

  62. Guo W: Functional data analysis in longitudinal settings using smoothing splines. Stat Methods Med Res. 2004, 13: 49-62. 10.1191/0962280204sm352ra.

    PubMed  Google Scholar 

  63. Laukaitis A: Functional data analysis for cash flow and transactions intensity continuous-time prediction using Hilbert-valued autoregressive processes. Eur J Oper Res. 2008, 185: 1607-1614. 10.1016/j.ejor.2006.08.030.

    Google Scholar 

  64. Bapna R, Jank W, Shmueli G: Price formation and its dynamics in online auctions. Decis Support Syst. 2008, 44: 641-656. 10.1016/j.dss.2007.09.004.

    Google Scholar 

  65. Koenig LL, Lucero JC, Perlman E: Speech production variability in fricatives of children and adults: results of functional data analysis. J Acoust Soc Am. 2008, 124: 3158-3170. 10.1121/1.2981639.

    PubMed  PubMed Central  Google Scholar 

  66. Harezlak J, Wu MC, Wang M, Schwartzman A, Christiani DC, Lin X: Biomarker discovery for arsenic exposure using functional data. Analysis and feature learning of mass spectrometry proteomic data. J Proteome Res. 2008, 7: 217-224. 10.1021/pr070491n.

    CAS  PubMed  Google Scholar 

  67. Chapados C, Levitin DJ: Cross-modal interactions in the experience of musical performances: physiological correlates. Cognition. 2008, 108: 639-651. 10.1016/j.cognition.2008.05.008.

    PubMed  Google Scholar 

  68. Song JJ, Lee H, Morris JS, Kang S: Clustering of time-course gene expression data using functional data analysis. Comp Bio Chem. 2007, 31: 265-274. 10.1016/j.compbiolchem.2007.05.006.

    CAS  Google Scholar 

  69. Vakorin VA, Borowsky R, Sarty GE: Characterizing the functional MRI response using Tikhonov regularization. Stat Med. 2007, 26: 3830-3844. 10.1002/sim.2981.

    PubMed  Google Scholar 

  70. Dabo-niang S, Vieu P: On the using of modal curves for radar waveforms classification. Comput Statist Data Anal. 2007, 51: 48-78.

    Google Scholar 

  71. Gao H: Day of week effects on diurnal ozone/NOx cycles and transportation emissions in Southern California. Transp Res Part D Transp Envr. 2007, 12: 292-305. 10.1016/j.trd.2007.03.004.

    Google Scholar 

  72. Meiring W: Oscillations and time trends in stratospheric ozone levels. J Am Stat Assoc. 2007, 102: 788-802. 10.1198/016214506000000825.

    CAS  Google Scholar 

  73. Meyer PM, Zeger SL, Harlow SD, Sowers M, Crawford S, Luborsky JL, Janssen I, McConnell DS, Randolph JF, Weiss G: Characterizing daily urinary hormone profiles for women at midlife using functional data analysis. Am J Epidemiol. 2007, 165: 936-945. 10.1093/aje/kwk090.

    PubMed  Google Scholar 

  74. West RM, Harris K, Gilthorpe MS, Tolman C, Will EJ: Functional data analysis applied to a randomized controlled clinical trial in hemodialysis patients describes the variability of patient responses in the control of renal anemia. J Am Soc Nephrol. 2007, 18: 2371-2376. 10.1681/ASN.2006050436.

    CAS  PubMed  Google Scholar 

  75. Harrison A, Ryan W, Hayes K: Functional data analysis of joint coordination in the development of vertical jump performance. Sports Biomech. 2007, 6: 199-214. 10.1080/14763140701323042.

    CAS  PubMed  Google Scholar 

  76. Leng X, Muller H: Classification using functional data analysis for temporal gene expression data. Bioinformatics. 2006, 22: 68-76. 10.1093/bioinformatics/bti742.

    CAS  PubMed  Google Scholar 

  77. Zhang Y, Muller HG, Carey JR, Papadopoulos NT: Behavioral trajectories as predictors in event history analysis: male calling behavior forecasts medfly longevity. Mech Ageing Dev. 2006, 127: 680-686. 10.1016/j.mad.2006.04.001.

    PubMed  PubMed Central  Google Scholar 

  78. Duhamel A, Devos P, Bourriez JL, Preda C, Defebvre L, Beuscart R: Functional data analysis for gait curves study in Parkinson’s disease. Stud Health Technol Inform. 2006, 124: 569-574.

    PubMed  Google Scholar 

  79. Ryan W, Harrison A, Hayes K: Functional data analysis of knee joint kinematics in the vertical jump. Sports Biomech. 2006, 5: 121-138. 10.1080/14763141.2006.9628228.

    PubMed  Google Scholar 

  80. Page A, Ayala G, Leon MT, Peydro MF, Prat JM: Normalizing temporal patterns to analyze sit-to-stand movements by using registration of functional data. J Biomech. 2006, 39: 2526-2534. 10.1016/j.jbiomech.2005.07.032.

    CAS  PubMed  Google Scholar 

  81. Henderson B: Exploring between site differences in water quality trends: a functional data analysis approach. Environmetrics. 2006, 17: 65-80. 10.1002/env.750.

    Google Scholar 

  82. Stewart K, Darcy D, Daniel S: Opportunities and challenges applying functional data analysis to the study of open source software evolution. Stat Sci. 2006, 21: 167-178. 10.1214/088342306000000141.

    Google Scholar 

  83. Lee S, Byrd D, KrivokapicÌ J: Functional data analysis of prosodic effects on articulatory timing. J Acoust Soc Am. 2006, 119: 1666-1671. 10.1121/1.2161436.

    PubMed  PubMed Central  Google Scholar 

  84. Newell J, McMillan K, Grant S, McCabe G: Using functional data analysis to summarise and interpret lactate curves. Comput Biol Med. 2006, 36: 262-275. 10.1016/j.compbiomed.2004.11.006.

    CAS  PubMed  Google Scholar 

  85. Vines BW, Krumhansl CL, Wanderley MM, Levitin DJ: Cross-modal interactions in the perception of musical performance. Cognition. 2006, 101: 80-113. 10.1016/j.cognition.2005.09.003.

    PubMed  Google Scholar 

  86. Bensmail H, Aruna B, Semmes OJ, Haoudi A: Functional clustering algorithm for high-dimensional proteomics data. J Biomed Biotechno. 2005, 2: 80-86.

    Google Scholar 

  87. Ormoneit D, Black MJ, Hastie T, Sidenbladh H: Representing cyclic human motion using functional analysis. Image Vision Comput. 2005, 23: 1264-1276. 10.1016/j.imavis.2005.09.004.

    Google Scholar 

  88. Viviani R, Gron G, Spitzer M: Functional principal component analysis of fMRI data. Hum Brain Mapp. 2005, 24: 109-129. 10.1002/hbm.20074.

    PubMed  Google Scholar 

  89. Long CJ, Brown EN, Triantafyllou C, Aharon I, Wald LL, Solo V: Nonstationary noise estimation in functional MRI. NeuroImage. 2005, 28: 890-903. 10.1016/j.neuroimage.2005.06.043.

    CAS  PubMed  Google Scholar 

  90. Manté C, Durbec JP, Dauvin JC: A functional data-analytic approach to the classification of species according to their spatial dispersion. Application to a marine macrobenthic community from the Bay of Morlaix (western English Channel). J Appl Stat. 2005, 32: 831-840. 10.1080/02664760500080124.

    Google Scholar 

  91. Rupp A: Quantifying subpopulation differences for a lack of invariance using complex examinee profiles: an exploratory multigroup approach using functional data analysis. Edu Res Eval. 2005, 11: 71-97. 10.1080/13803610500110430.

    Google Scholar 

  92. Laukaitis A: Functional data analysis for clients segmentation tasks. Eur J Oper Res. 2005, 163: 210-216. 10.1016/j.ejor.2004.01.010.

    Google Scholar 

  93. Lucero JC: Comparison of measures of variability of speech movement trajectories using synthetic records. J Speech Lang Hear Res. 2005, 48: 336-344. 10.1044/1092-4388(2005/023).

    PubMed  Google Scholar 

  94. Vines BW, Nuzzo RL, Levitin DJ: Analysing temporal dynamics in music. Music Percept. 2005, 23: 137-152. 10.1525/mp.2005.23.2.137.

    Google Scholar 

  95. Hutchinson RA, McLellan PJ, Ramsay JO, Sulieman H, Bacon DW: Investigating the impact of operating parameters on molecular weight distributions using functional regression. Macromol Symp. 2004, 206: 495-508. 10.1002/masy.200450238.

    CAS  Google Scholar 

  96. Stier AW, Stein HJ, Schwaiger M, Heidecke CD: Modeling of esophageal bolus flow by functional data analysis of scintigrams. Dis Esophagus. 2004, 17: 51-57. 10.1111/j.1442-2050.2004.00373.x.

    PubMed  Google Scholar 

  97. Liggett WS, Barker PE, Semmes OJ, Cazares LH: Measurement reproducibility in the early stages of biomarker development. Dis Markers. 2004, 20: 295-307.

    CAS  PubMed  Google Scholar 

  98. Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ: A unified approach for morphometric and functional data analysis in young, old, and demented adults using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage. 2004, 23: 724-738. 10.1016/j.neuroimage.2004.06.018.

    PubMed  Google Scholar 

  99. McAdams S: Influences of large-scale form on continuous ratings in response to a contemporary piece in a live concert setting. Music Percept. 2004, 22: 297-350. 10.1525/mp.2004.22.2.297.

    Google Scholar 

  100. Spitzner DJ, Marron JS, Essick GK: Mixed-model functional ANOVA for studying human tactile perception. J Am Stat Assoc. 2003, 98: 263-272. 10.1198/016214503000035.

    Google Scholar 

  101. Yao F, Muller HG, Clifford AJ, Dueker SR, Follett J, Lin Y, Buchholz BA, Vogel JS: Shrinkage estimation for functional principal component scores with application to the population kinetics of plasma folate. Biometrics. 2003, 59: 676-685. 10.1111/1541-0420.00078.

    PubMed  Google Scholar 

  102. Ogden RT, Miller CE, Takezawa K, Ninomiya S: Functional regression in crop lodging assessment with digital images. J Agric Biol Environ Stat. 2002, 7: 389-402. 10.1198/108571102339.

    Google Scholar 

  103. Clarysse P, Han M, Croisille P, Magnin IE: Exploratory analysis of the spatio-temporal deformation of the myocardium during systole from tagged MRI. IEEE Trans Biomed Eng. 2002, 49: 1328-1339. 10.1109/TBME.2002.804587.

    PubMed  Google Scholar 

  104. Ramsay JO, Ramsey BJ: Functional data analysis of the dynamics of the monthly index of nondurable goods production. J Econ. 2002, 107: 327-344.

    Google Scholar 

  105. Hall P, Poskitt D, Presnell B: A functional data-analytic approach to signal discrimination. Technometrics. 2001, 43: 1-24. 10.1198/00401700152404273.

    Google Scholar 

  106. Lucero JC, Koenig LL: Time normalization of voice signals using functional data analysis. J Acoust Soc Am. 2000, 108: 1408-1420. 10.1121/1.1289206.

    CAS  PubMed  Google Scholar 

  107. Besse PC, Cardot H, Stephenson DB: Autoregressive forecasting of some functional climatic variations. Scand Stat Theory Appl. 2000, 27: 673-687.

    Google Scholar 

  108. Lucero JC: Computation of the harmonics-to-noise ratio of a voice signal using a functional data analysis algorithm. J Sound Vibrat. 1999, 222: 512-520. 10.1006/jsvi.1998.2072.

    Google Scholar 

  109. Bjornstad O, Chr SN, Saitoh T, Lingjaerde OC: Mapping the regional transition to cyclicity in clethrionomys rufocanus: spectral densities and functional data analysis. Res Pop Ecol. 1998, 40: 77-84. 10.1007/BF02765223.

    Google Scholar 

  110. Ramsay JO, Munhall KG, Gracco VL, Ostry DJ: Functional data analyses of lip motion. J Acoust Soc Am. 1996, 99: 3718-3727. 10.1121/1.414986.

    CAS  PubMed  Google Scholar 

  111. Ramsay JO, Wang X: A functional data analysis of the pinch force of human fingers. App Stat. 1995, 44: 17-30. 10.2307/2986192.

    Google Scholar 

  112. Grambsch PM, Randall BL, Bostick RM, Potter JD, Louis TA: Modeling the labeling index distribution: An application of functional data analysis. J Am Stat Assoc. 1995, 90: 813-821. 10.1080/01621459.1995.10476579.

    Google Scholar 

  113. Eubank RL: Nonparametric regression and spline smoothing. 1999, New York: Marcel Dekker

    Google Scholar 

  114. Marx BD, Eilers PHC: Direct generalized additive modeling with penalized likelihood. Comput Stat Data Anal. 1998, 28: 193-209. 10.1016/S0167-9473(98)00033-4.

    Google Scholar 

  115. Wood SN: Modelling and smoothing parameter estimation with multiple quadratic penalties. J R Stat Soc Series B Stat Methodol. 2000, 62: 413-428. 10.1111/1467-9868.00240.

    Google Scholar 

  116. Gao H, Niemeier D: Using functional data analysis of diurnal ozone and NOx cycles to inform transportation emissions control. Transp Res Part D Transp Envr. 2008, 13: 221-238. 10.1016/j.trd.2008.02.003.

    Google Scholar 

  117. Ruppert D, Wand MP, Carroll RJ: Semiparametric regression. 2003, Cambridge: New York

    Google Scholar 

  118. Simonoff JS: Smoothing methods in statistics. 1996, New York: Springer

    Google Scholar 

  119. Croux C, RuizGazen A: High breakdown estimators for principal components: The project-pursuit approach revisited. J Multivar Anal. 2005, 95: 206-226. 10.1016/j.jmva.2004.08.002.

    Google Scholar 

  120. Ferraty F, Vieu P: Nonparametric functional data analysis. 2006, New York: Springer

    Google Scholar 

  121. Kneip A, Utikal KJ: Inference for density families using functional principal component analysis. J Am Stat Assoc. 2001, 94: 519-533.

    Google Scholar 

  122. James GM, Hastie TJ, Sugar CA: Principal component models for sparse functional data. Biometrika. 2001, 87: 587-602.

    Google Scholar 

  123. Wang H, Neill J, Miller F: Nonparametric clustering of functional data. Stat Interface. 2008, 1: 47-62.

    Google Scholar 

  124. Dudoit S, Fridlyand J, Speed TP: Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc. 2002, 97: 77-87. 10.1198/016214502753479248.

    CAS  Google Scholar 

  125. Lee JW, Lee JB, Park M, Song SH: An extensive comparison of recent classification tools applied to microarray data. Comput Statist Data Anal. 2005, 48: 869-885. 10.1016/j.csda.2004.03.017.

    Google Scholar 

  126. Denoeux T: A K-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans Syst Man Cybernet. 1995, 25: 804-813. 10.1109/21.376493.

    Google Scholar 

  127. Keller JM, Gray MR, Givens JA: A fuzzy k-nearest neighbours algorithm. IEEE Trans. Syst. ManCybern. 1985, 15: 580-585.

    Google Scholar 

  128. Cortes C, Vapnik V: Support-vector networks. Mach Learn. 1995, 20: 273-297.

    Google Scholar 

  129. Fraley C, Raftery AE: Model-based clustering, discriminant analysis, and density estimation. J Am Stat Assoc. 2002, 97: 611-631. 10.1198/016214502760047131.

    Google Scholar 

  130. Hyndman RJ, Koehler AB, Snyder RD, Grose S: A state space framework for automatic forecasting using exponential smoothing methods. Int J Forecast. 2002, 18: 439-454. 10.1016/S0169-2070(01)00110-8.

    Google Scholar 

  131. Santen G, van Zwet E, Danhof M, Pasqua OD: Heterogeneity in patient response in depression: The relevance of functional data analysis. 2008, Universiteit Leiden, Netherland: PhD Thesis

    Google Scholar 

  132. Finch CF, Hayen A: Governmental health agencies need to assume leadership in injury prevention. Inj Prev. 2006, 12: 2-3. 10.1136/ip.2005.010587.

    CAS  PubMed  PubMed Central  Google Scholar 

  133. Lee RD, Carter LR: Modeling and forecasting U.S. mortality. J Am Stat Assoc. 1992, 87: 659-675.

    Google Scholar 

  134. Lee RD, Miller T: Evaluating the performance of the Lee-Carter method for forecasting mortality. Demography. 2001, 38: 537-549. 10.1353/dem.2001.0036.

    CAS  PubMed  Google Scholar 

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Acknowledgements

The study was funded (at least in part) through the Early Career Researcher development funding program at the University of Ballarat. Professor Caroline Finch was supported by a National Health and Medical Research Council (NHMRC) Principal Research Fellowship (ID: 565900). Peter Richardson and Dr Saad Saleem assisted with the original literature searches and retrieval of published articles for the review. Eileen Clark proofread and copy edited the manuscript.

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  1. Flinders Centre for Epidemiology and Biostatistics, School of Medicine, Faculty of Health Sciences, Flinders University, Adelaide, SA, 5001, Australia

    Shahid Ullah

  2. Centre for Healthy and Safe Sports (CHASS), University of Ballarat, SMB Campus, Ballarat, VIC, 3353, Australia

    Caroline F Finch

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  1. Shahid Ullah
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As first author, SU conceived and designed the study reported in this paper. He took the lead role in drafting the manuscript and reviewed all relevant articles and analysed their content. The second author, CF, provided expertise in the conduct of systematic reviews and also contributed to the writing and editing of the paper. She reviewed the article and revised it critically for important intellectual content. Both authors read and approved the final manuscript.

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Ullah, S., Finch, C.F. Applications of functional data analysis: A systematic review. BMC Med Res Methodol 13, 43 (2013). https://doi.org/10.1186/1471-2288-13-43

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