Sparse functional varying-coefficient mixture regression
References
[1]
Apostolova L.G., Mosconi L., Thompson P.M., et al., Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal, Neurobiol. Aging 31 (7) (2010) 1077–1088.
[2]
Bosq D., Linear Processes in Function Spaces: Theory and Applications, Springer, New York, 2000.
[3]
Bouveyron C., Jacques J., Schmutz A., et al., Co-clustering of multivariate functional data for the analysis of air pollution in the south of France, Ann. Appl. Stat. 16 (3) (2022) 1400–1422.
[4]
Carrasquillo M.M., Zou F., Pankratz V.S., et al., Genetic variation in PCDH11X is associated with susceptibility to late-onset Alzheimer’s disease, Nat. Genet. 41 (2) (2009) 192–198.
[5]
Chen X., Fan Y., Tsyrennikov V., Efficient estimation of semiparametric multivariate copula models, J. Amer. Statist. Assoc. 101 (475) (2006) 1228–1240.
[6]
Delaigle A., Hall P., Pham T., Clustering functional data into groups by using projections, J. R. Stat. Soc. Ser. B Stat. Methodol. 81 (2) (2019) 271–304.
[7]
Ferraty F., Vieu P., Nonparametric Functional Data Analysis: Theory and Practice, Springer Science & Business Media, 2006.
[8]
Friedman J., Hastie T., Tibshirani R., A note on the group lasso and a sparse group lasso, 2010, arXiv preprint arXiv:1001.0736.
[9]
Frisoni G.B., Ganzola R., Canu E., et al., Mapping local hippocampal changes in Alzheimer’s disease and normal ageing with MRI at 3 Tesla, Brain 131 (12) (2008) 3266–3276.
[10]
Hall P., Hosseini-Nasab M., On properties of functional principal components analysis, J. R. Stat. Soc. Ser. B Stat. Methodol. 68 (1) (2006) 109–126.
[11]
Happ C., Greven S., Multivariate functional principal component analysis for data observed on different (dimensional) domains, J. Amer. Statist. Assoc. 113 (522) (2018) 649–659.
[12]
Horváth L., Kokoszka P., Inference for Functional Data with Applications, Springer Science & Business Media, 2012.
[13]
Huang J., Ma S., Xie H., et al., A group bridge approach for variable selection, Biometrika 96 (2) (2009) 339–355.
[14]
Huang T., Peng H., Zhang K., Model selection for Gaussian mixture models, Statist. Sinica (2017) 147–169.
[15]
Huang C., Zhu H., Functional hybrid factor regression model for handling heterogeneity in imaging studies, Biometrika 109 (4) (2022) 1133–1148.
[16]
James G.M., Wang J., Zhu J., Functional linear regression that’s interpretable, Ann. Statist. 37 (5A) (2009) 2083–2108.
[17]
Li X., Wang L., Wang H.J., et al., Sparse learning and structure identification for ultrahigh-dimensional image-on-scalar regression, J. Amer. Statist. Assoc. 116 (536) (2021) 1994–2008.
[18]
Li T., Yu Y., Marron J.S., et al., A partially functional linear modeling framework for integrating genetic, imaging, and clinical data, 2022, arXiv preprint arXiv:2210.01084.
[19]
Lin Z., Wang L., Cao J., Interpretable functional principal component analysis, Biometrics 72 (3) (2016) 846–854.
[20]
Luo X., Zhu L., Zhu H., Single-index varying coefficient model for functional responses, Biometrics 72 (4) (2016) 1275–1284.
[21]
Ma L., Hu T., Sun J., Sieve maximum likelihood regression analysis of dependent current status data, Biometrika 102 (3) (2015) 731–738.
[22]
Mielke M.M., Vemuri P., Rocca W.A., Clinical epidemiology of Alzheimer’s disease: assessing sex and gender differences, Clin. Epidemiol. 6 (2014) 37.
[23]
Olazarán J., Reisberg B., Clare L., et al., Nonpharmacological therapies in Alzheimer’s disease: a systematic review of efficacy, Dement. Geriatr. Cogn. Disord. 30 (2) (2010) 161–178.
[24]
Park J., Ahn J., Jeon Y., Sparse functional linear discriminant analysis, Biometrika 109 (1) (2022) 209–226.
[25]
Patenaude B., Smith S.M., Kennedy D.N., et al., A Bayesian model of shape and appearance for subcortical brain segmentation, Neuroimage 56 (3) (2011) 907–922.
[26]
Pei Y., Peng H., Xu J., A latent class Cox model for heterogeneous time-to-event data, J. Econometrics 239 (2) (2022).
[27]
Pollard D., Convergence of Stochastic Processes, David Pollard, 1984.
[28]
Ramsay J.O., Silverman B.W., Functional Data Analysis, Springer, 2005.
[29]
Schumacker L., Spline Functions: Basic Theory, Wiley, New York, 1981.
[30]
Serban N., Jiang H., Multilevel functional clustering analysis, Biometrics 68 (3) (2012) 805–814.
[31]
Shen X., Wong W.H., Convergence rate of sieve estimates, Ann. Statist. 22 (2) (1994) 580–615.
[32]
Shi J., Thompson P.M., Gutman B., et al., Surface fluid registration of conformal representation: Application to detect disease burden and genetic influence on hippocampus, NeuroImage 78 (2013) 111–134.
[33]
Tu C.Y., Park J., Wang H., Estimation of functional sparsity in nonparametric varying coefficient models for longitudinal data analysis, Statist. Sinica 30 (1) (2020) 439–465.
[34]
Van der Vaart A.W., Wellner J.A., Weak Convergence and Empirical Processes, Springer, 1996.
[35]
Van De Geer S., Empirical Processes in M-Estimation, Cambridge UP, 2000.
[36]
Wang H., Kai B., Functional sparsity: Global versus local, Statist. Sinica 25 (4) (2015) 1337–1354.
[37]
Wang H., Li R., Tsai C.-L., Tuning parameter selectors for the smoothly clipped absolute deviation method, Biometrika 94 (3) (2007) 553–568.
[38]
Wang Y., Song Y., Rajagopalan P., et al., Surface-based TBM boosts power to detect disease effects on the brain: an N=804 ADNI study, Neuroimage 56 (4) (2011) 1993–2010.
[39]
Wong R.K., Li Y., Zhu Z., Partially linear functional additive models for multivariate functional data, J. Amer. Statist. Assoc. 114 (525) (2019) 406–418.
[40]
Xie H., Huang J., SCAD-penalized regression in high-dimensional partially linear models, Ann. Statist. 37 (2) (2009) 673–696.
[41]
Yao F., Fu Y., Lee T.C., Functional mixture regression, Biostatistics 12 (2) (2011) 341–353.
[42]
Yu D., Wang L., Kong D., et al., Mapping the genetic-imaging-clinical pathway with applications to alzheimer’s disease, J. Amer. Statist. Assoc. 117 (540) (2022) 1656–1668.
[43]
Zhang X., Wang J.-L., Varying-coefficient additive models for functional data, Biometrika 102 (1) (2015) 15–32.
[44]
Zhong Q., Lin H., Li Y., Cluster non-Gaussian functional data, Biometrics 77 (3) (2021) 852–865.
[45]
Zhong Q., Liu W., Liu L., Liang H., Lin H., Generalized functional feature regression models, Stat. Sin. (2023),.
[46]
Zhou F., Zhou H., Li T., et al., Analysis of secondary phenotypes in multigroup association studies, Biometrics 76 (2) (2020) 606–618.
[47]
Zhu H., Li R., Kong L., Multivariate varying coefficient model for functional responses, Ann. Stat. 40 (5) (2012) 2634.
[48]
Zhu H., Yao F., Zhang H.H., Structured functional additive regression in reproducing kernel Hilbert spaces, J. R. Stat. Soc. Ser. B Stat. Methodol. 76 (3) (2014) 581–603.
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Published: 20 February 2025
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