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Relevant bibliographies by topics / Functional data analysis / Journal articles

Journal articles on the topic 'Functional data analysis'

To see the other types of publications on this topic, follow the link: Functional data analysis.

Author: Grafiati

Published: 4 June 2021

Last updated: 7 July 2024

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1

MIZUTA, Masahiro. "Functional Data and Functional Data Analysis." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 17, no. 4 (2005): 413–17. http://dx.doi.org/10.3156/jsoft.17.413.

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2

Wang, Jane-Ling, Jeng-Min Chiou, and Hans-Georg Müller. "Functional Data Analysis." Annual Review of Statistics and Its Application 3, no. 1 (June 2016): 257–95. http://dx.doi.org/10.1146/annurev-statistics-041715-033624.

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3

Marron, J. S., J. O. Ramsey, and B. W. Silverman. "Functional Data Analysis." Journal of the American Statistical Association 93, no. 443 (September 1998): 1232. http://dx.doi.org/10.2307/2669864.

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4

Georgiev, Alexander A. "Functional Data Analysis." Technometrics 40, no. 3 (August 1998): 260–61. http://dx.doi.org/10.1080/00401706.1998.10485535.

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5

Muraki, Eiji, J. O. Ramsay, and B. W. Silverman. "Functional Data Analysis." Journal of Educational and Behavioral Statistics 24, no. 1 (1999): 101. http://dx.doi.org/10.2307/1165264.

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6

Ahn, Kyungmin. "Comparative study between functional data analysis and multivariate data analysis for functional data." Journal of the Korean Data And Information Science Society 33, no. 5 (September 30, 2022): 817–27. http://dx.doi.org/10.7465/jkdi.2022.33.5.817.

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7

Yamanishi, Yoshihiro, and Yutaka Tanaka. "8. Functional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 307–17. http://dx.doi.org/10.5183/jjscs1988.15.2_307.

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8

Tokushige, Shuichi, Koichi Inada, and Hiroshi Yadohisa. "8. Functional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 319–26. http://dx.doi.org/10.5183/jjscs1988.15.2_319.

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9

Mizuta, Masahiro. "8. Functional Data Analysis." Journal of the Japanese Society of Computational Statistics 15, no. 2 (2003): 327–33. http://dx.doi.org/10.5183/jjscs1988.15.2_327.

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10

Altland, Henry W. "Applied Functional Data Analysis." Technometrics 45, no. 1 (February 2003): 101–2. http://dx.doi.org/10.1198/tech.2003.s16.

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11

Park, So Young, and Ana-Maria Staicu. "Longitudinal functional data analysis." Stat 4, no. 1 (February 2015): 212–26. http://dx.doi.org/10.1002/sta4.89.

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12

Epifanio, Irene, and Noelia Ventura-Campos. "Functional data analysis in shape analysis." Computational Statistics & Data Analysis 55, no. 9 (September 2011): 2758–73. http://dx.doi.org/10.1016/j.csda.2011.04.003.

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13

Barati, Zeinab, Issa Zakeri, and Kambiz Pourrezaei. "Functional data analysis view of functional near infrared spectroscopy data." Journal of Biomedical Optics 18, no. 11 (November 18, 2013): 117007. http://dx.doi.org/10.1117/1.jbo.18.11.117007.

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14

Yao, Fang, Hans-Georg Müller, and Jane-Ling Wang. "Functional Data Analysis for Sparse Longitudinal Data." Journal of the American Statistical Association 100, no. 470 (June 2005): 577–90. http://dx.doi.org/10.1198/016214504000001745.

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15

Shen, Dan, Haipeng Shen, Shankar Bhamidi, Yolanda Muñoz Maldonado, Yongdai Kim, and J. S. Marron. "Functional Data Analysis of Tree Data Objects." Journal of Computational and Graphical Statistics 23, no. 2 (April 3, 2014): 418–38. http://dx.doi.org/10.1080/10618600.2013.786943.

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16

Chen, Yakuan, Jeff Goldsmith, and R. Todd Ogden. "Functional Data Analysis of Dynamic PET Data." Journal of the American Statistical Association 114, no. 526 (October 26, 2018): 595–609. http://dx.doi.org/10.1080/01621459.2018.1497495.

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17

Aguilera, Ana M., Manuel Escabias, Mariano J. Valderrama, and M. Carmen Aguilera-Morillo. "Functional Analysis of Chemometric Data." Open Journal of Statistics 03, no. 05 (2013): 334–43. http://dx.doi.org/10.4236/ojs.2013.35039.

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18

Levitin, Daniel J., Regina L. Nuzzo, Bradley W. Vines, and J. O. Ramsay. "Introduction to functional data analysis." Canadian Psychology/Psychologie canadienne 48, no. 3 (2007): 135–55. http://dx.doi.org/10.1037/cp2007014.

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19

Lin, Zhenhua, and Fang Yao. "Intrinsic Riemannian functional data analysis." Annals of Statistics 47, no. 6 (December 2019): 3533–77. http://dx.doi.org/10.1214/18-aos1787.

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20

Xu, Li, and Yili Hong. "Functional and Shape Data Analysis." Journal of Quality Technology 49, no. 4 (October 2017): 419–20. http://dx.doi.org/10.1080/00224065.2017.11918007.

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21

Müller, Hans-Georg, Rituparna Sen, and Ulrich Stadtmüller. "Functional data analysis for volatility." Journal of Econometrics 165, no. 2 (December 2011): 233–45. http://dx.doi.org/10.1016/j.jeconom.2011.08.002.

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22

Hosseini-Nasab, Mohammad, and Zahra Mirzaei K. "Functional analysis of glaucoma data." Statistics in Medicine 33, no. 12 (December 26, 2013): 2077–102. http://dx.doi.org/10.1002/sim.6061.

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23

DE SANCTIS, ANGELA, and TONIO DI BATTISTA. "FUNCTIONAL ANALYSIS FOR PARAMETRIC FAMILIES OF FUNCTIONAL DATA." International Journal of Bifurcation and Chaos 22, no. 09 (September 2012): 1250226. http://dx.doi.org/10.1142/s0218127412502264.

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Assuming a Parametric Family of Functional Data, the problem of computing summary statistics of the same functional form is investigated. The central idea is to compile the statistics on the parameters instead of on the functions themselves. With the hypothesis of a monotonic dependence from parameters, we highlight the special features of this statistics.

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24

Kim, Jong-Min, Nak-Kyeong Kim, Yoonsung Jung, and Sunghae Jun. "Patent data analysis using functional count data model." Soft Computing 23, no. 18 (August 23, 2018): 8815–26. http://dx.doi.org/10.1007/s00500-018-3481-6.

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25

Birbilas, Algimantas, and Alfredas Račkauskas. "FUNCTIONAL MODELLING OF TELECOMMUNICATIONS DATA." Mathematical Modelling and Analysis 27, no. 1 (February 7, 2022): 117–33. http://dx.doi.org/10.3846/mma.2022.14043.

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This work deals with statistical modeling and forecasting of telecommunications data. Main mobile traffic events (SMS, Voice calls, Mobile data) are smoothed using B-spline functions and later analyzed in a functional framework. Functional linear auto-regression models are fitted using both bottom-up and topdown design methodologies. The advantages and disadvantages of both approaches for the prediction of mobile telephone users’ habits are discussed.

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26

Krzyśko, Mirosław, and Łukasz Waszak. "Canonical correlation analysis for functional data." Biometrical Letters 50, no. 2 (December 1, 2013): 95–105. http://dx.doi.org/10.2478/bile-2013-0020.

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Summary Classical canonical correlation analysis seeks the associations between two data sets, i.e. it searches for linear combinations of the original variables having maximal correlation. Our task is to maximize this correlation, and is equivalent to solving a generalized eigenvalue problem. The maximal correlation coefficient (being a solution of this problem) is the first canonical correlation coefficient. In this paper we propose a new method of constructing canonical correlations and canonical variables for a pair of stochastic processes represented by a finite number of orthonormal basis functions.

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27

Descary, Marie-Hélène, and Victor M. Panaretos. "Functional data analysis by matrix completion." Annals of Statistics 47, no. 1 (February 2019): 1–38. http://dx.doi.org/10.1214/17-aos1590.

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28

Shin, Hyejin, and Tailen Hsing. "Linear prediction in functional data analysis." Stochastic Processes and their Applications 122, no. 11 (November 2012): 3680–700. http://dx.doi.org/10.1016/j.spa.2012.06.014.

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29

Lazar, Nicole. "The Big Picture: Functional Data Analysis." CHANCE 27, no. 1 (January 2, 2014): 38–40. http://dx.doi.org/10.1080/09332480.2014.890868.

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30

Cremona, Marzia A., Hongyan Xu, Kateryna D. Makova, Matthew Reimherr, Francesca Chiaromonte, and Pedro Madrigal. "Functional data analysis for computational biology." Bioinformatics 35, no. 17 (January 22, 2019): 3211–13. http://dx.doi.org/10.1093/bioinformatics/btz045.

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31

Somorjai, R. L. "Exploratory data analysis in functional neuroimaging." Artificial Intelligence in Medicine 25, no. 1 (May 2002): 1–3. http://dx.doi.org/10.1016/s0933-3657(02)00004-0.

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32

王, 馨彤. "Linear Discriminant Analysis for Functional Data." Operations Research and Fuzziology 09, no. 02 (2019): 156–64. http://dx.doi.org/10.12677/orf.2019.92018.

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33

Ramsay, J. O., and C. J. Dalzell. "Some Tools for Functional Data Analysis." Journal of the Royal Statistical Society: Series B (Methodological) 53, no. 3 (July 1991): 539–61. http://dx.doi.org/10.1111/j.2517-6161.1991.tb01844.x.

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34

Martínez-Camblor, Pablo, and Norberto Corral. "Repeated measures analysis for functional data." Computational Statistics & Data Analysis 55, no. 12 (December 2011): 3244–56. http://dx.doi.org/10.1016/j.csda.2011.06.007.

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35

Kokoszka, Piotr, Hanny Oja, Byeong Park, and Laura Sangalli. "Special issue on functional data analysis." Econometrics and Statistics 1 (January 2017): 99–100. http://dx.doi.org/10.1016/j.ecosta.2016.11.003.

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36

Basna, Rani, Hiba Nassar, and Krzysztof Podgórski. "Data driven orthogonal basis selection for functional data analysis." Journal of Multivariate Analysis 189 (May 2022): 104868. http://dx.doi.org/10.1016/j.jmva.2021.104868.

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37

NimaHashemiGhermezi, Syyed, Taher Taherian, and Farhad Hosseinzadeh Lotfi. "Data Envelopment Analysis with Functional Data using Preference Method." International Journal of Computer Applications 55, no. 14 (October 20, 2012): 48–53. http://dx.doi.org/10.5120/8827-2908.

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38

Sugianto, S., M. Rusdi, and Syakur. "Functional Data Analysis: An Initiative Approach for Hyperspectral Data." Journal of Physics: Conference Series 1363 (November 2019): 012087. http://dx.doi.org/10.1088/1742-6596/1363/1/012087.

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39

Kara, Lydia-Zaitri, Ali Laksaci, Mustapha Rachdi, and Philippe Vieu. "Data-driven kNN estimation in nonparametric functional data analysis." Journal of Multivariate Analysis 153 (January 2017): 176–88. http://dx.doi.org/10.1016/j.jmva.2016.09.016.

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40

Chen, Kehui, Xiaoke Zhang, Alexander Petersen, and Hans-Georg Müller. "Quantifying Infinite-Dimensional Data: Functional Data Analysis in Action." Statistics in Biosciences 9, no. 2 (November 20, 2015): 582–604. http://dx.doi.org/10.1007/s12561-015-9137-5.

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41

Akgün, Devrim, Ünal Sakoğlu, Johnny Esquivel, Bryon Adinoff, and Mutlu Mete. "GPU accelerated dynamic functional connectivity analysis for functional MRI data." Computerized Medical Imaging and Graphics 43 (July 2015): 53–63. http://dx.doi.org/10.1016/j.compmedimag.2015.02.009.

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42

Kokoszka, Piotr. "Dependent Functional Data." ISRN Probability and Statistics 2012 (October 16, 2012): 1–30. http://dx.doi.org/10.5402/2012/958254.

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This paper reviews recent research on dependent functional data. After providing an introduction to functional data analysis, we focus on two types of dependent functional data structures: time series of curves and spatially distributed curves. We review statistical models, inferential methodology, and possible extensions. The paper is intended to provide a concise introduction to the subject with plentiful references.

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43

Zao, Yangxinzi. "Functional Data Clustering Via Functional Mahalanobis Distance." Highlights in Science, Engineering and Technology 70 (November 15, 2023): 31–41. http://dx.doi.org/10.54097/hset.v70i.12137.

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As an exploratory data analysis method, functional data clustering aims to identify the underlying features of the observed data. In this context, this paper proposes a functional data clustering method based on functional Mahalanobis distance. As a distance-based non-parametric clustering model, the proposed method can effectively avoid the disadvantages of generative models and has excellent properties of decoupling and dimension standardization. Compared with other functional data clustering models, this method has lower computational complexity. In addition, the proposed method can be applied to any distance-based multivariate clustering method, thus generalizing it to the case of functional data. In practical data analysis, this paper compares the performance of this method with some other functional clustering methods, using k-means clustering as an example, and finds that it has better performance in terms of purity, adjusted Rand index, and computational speed. Finally, the idea of using Mahalanobis distance for functional data distance measurement can also be extended to construct kernel functions for measuring similarity between functional data samples, thus developing non-linear functional data analysis methods based on reproducing kernel theory.

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44

Hsieh, I.-Chung, and Yufen Huang. "Sensitivity analysis and visualization for functional data." Journal of Statistical Computation and Simulation 91, no. 8 (February 26, 2021): 1593–615. http://dx.doi.org/10.1080/00949655.2020.1863405.

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45

Górecki, Tomasz, Mirosław Krzyśko, and Waldemar Wołyński. "Generalized canonical correlation analysis for functional data." Biometrical Letters 57, no. 1 (June 1, 2020): 1–12. http://dx.doi.org/10.2478/bile-2020-0001.

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SummaryThere is a growing need to analyze data sets characterized by several sets of variables observed on the same set of individuals. Such complex data structures are known as multiblock (or multiple-set) data sets. Multi-block data sets are encountered in diverse fields including bioinformatics, chemometrics, food analysis, etc. Generalized Canonical Correlation Analysis (GCCA) is a very powerful method to study this kind of relationships between blocks. It can also be viewed as a method for the integration of information from K > 2 distinct sources (Takane and Oshima-Takane 2002). In this paper, GCCA is considered in the context of multivariate functional data. Such data are treated as realizations of multivariate random processes. GCCA is a technique that allows the joint analysis of several sets of data through dimensionality reduction. The central problem of GCCA is to construct a series of components aiming to maximize the association among the multiple variable sets. This method will be presented for multivariate functional data. Finally, a practical example will be discussed.

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46

Ogul, Hasan, and Mahinur S. Akkaya. "Data Integration in Functional Analysis of MicroRNAs." Current Bioinformatics 6, no. 4 (December 1, 2011): 462–72. http://dx.doi.org/10.2174/157489311798072945.

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47

Nikitovic, Vladimir. "Functional data analysis in forecasting Serbian fertility." Stanovnistvo 49, no. 2 (2011): 73–89. http://dx.doi.org/10.2298/stnv1102073n.

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A new approach, combining functional data analysis and principal components decomposition in order to forecasting demographic rates, introduced recently by Hyndman and his associates, is tested on official data series of Serbian age-specific fertility rates available for period 1950-2009. The original concept of the method with its extensions and improvements is applied to region-specific data for the country (Central Serbia and Vojvodina). One of the most important benefits of the method reflected in confirmation that is essentially to model and forecast more than one principal component in order to adequately address sources of variation in fertility. Similarly, modelling and forecasting fertility rates with regards to age and not total fertility rates shows how important it is for the recognized tendency of postponing childbearing in Serbia to be included in coefficients of functional time series. Besides, the method is based completely on evaluation of historical data, without subjective views of forecasters having to be taken into account. Coherent functional product ratio forecasts of two regions proved to be highly convergent on the long-term not allowing for outliers to contaminate the forecast.

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48

Ferraty, Frederic, Alois Kneip, Piotr Kokoszka, and Alexander Petersen. "2nd Special issue on Functional Data Analysis." Econometrics and Statistics 21 (January 2022): 112–13. http://dx.doi.org/10.1016/j.ecosta.2021.11.003.

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49

Aneiros, Germán, Ivana Horová, Marie Hušková, and Philippe Vieu. "On functional data analysis and related topics." Journal of Multivariate Analysis 189 (May 2022): 104861. http://dx.doi.org/10.1016/j.jmva.2021.104861.

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50

Kim, Dongwoo, Young Kyung Lee, and Byeong U. Park. "Principal component analysis for Hilbertian functional data." Communications for Statistical Applications and Methods 27, no. 1 (January 31, 2020): 149–61. http://dx.doi.org/10.29220/csam.2020.27.1.149.

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