Sparse logistic functional principal component analysis for binary data

被引:0
|
作者
Rou Zhong
Shishi Liu
Haocheng Li
Jingxiao Zhang
机构
[1] Renmin University of China,Center for Applied Statistics, School of Statistics
[2] Hangzhou Dianzi University,School of Economics
[3] University of Calgary,Department of Mathematics and Statistics
来源
Statistics and Computing | 2023年 / 33卷
关键词
Functional principal component analysis; Penalized Bernoulli likelihood; Binary data; Local sparsity; MM algorithm;
D O I
暂无
中图分类号
学科分类号
摘要
Functional binary datasets occur frequently in real practice, whereas discrete characteristics of the data can bring challenges to model estimation. In this paper, we propose a sparse logistic functional principal component analysis (SLFPCA) method to handle functional binary data. The SLFPCA looks for local sparsity of the eigenfunctions to obtain convenience in interpretation. We formulate the problem through a penalized Bernoulli likelihood with both roughness penalty and sparseness penalty terms. An innovative algorithm is developed for the optimization of the penalized likelihood using majorization-minimization algorithm. The proposed method is accompanied by R package SLFPCA for implementation. The theoretical results indicate both consistency and sparsistency of the proposed method. We conduct a thorough numerical experiment to demonstrate the advantages of the SLFPCA approach. Our method is further applied to a physical activity dataset.
引用
收藏
相关论文
共 50 条
  • [11] Unified Principal Component Analysis for Sparse and Dense Functional Data under Spatial Dependency
    Zhang, Haozhe
    Li, Yehua
    JOURNAL OF BUSINESS & ECONOMIC STATISTICS, 2022, 40 (04) : 1523 - 1537
  • [12] Sparse and integrative principal component analysis for multiview data
    Xiao, Lin
    Xiao, Luo
    ELECTRONIC JOURNAL OF STATISTICS, 2024, 18 (02): : 3774 - 3824
  • [13] Sparse functional principal component analysis in a new regression framework
    Nie, Yunlong
    Cao, Jiguo
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 2020, 152
  • [14] Sparse principal component analysis
    Zou, Hui
    Hastie, Trevor
    Tibshirani, Robert
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2006, 15 (02) : 265 - 286
  • [15] Robust principal component analysis for functional data
    Peña, D
    Prieto, J
    TEST, 1999, 8 (01) : 56 - 60
  • [16] Principal component analysis for Hilbertian functional data
    Kim, Dongwoo
    Lee, Young Kyung
    Park, Byeong U.
    COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, 2020, 27 (01) : 149 - 161
  • [17] Functional principal component analysis of fMRI data
    Viviani, R
    Grön, G
    Spitzer, M
    HUMAN BRAIN MAPPING, 2005, 24 (02) : 109 - 129
  • [18] Robust principal component analysis for functional data
    N. Locantore
    J. S. Marron
    D. G. Simpson
    N. Tripoli
    J. T. Zhang
    K. L. Cohen
    Graciela Boente
    Ricardo Fraiman
    Babette Brumback
    Christophe Croux
    Jianqing Fan
    Alois Kneip
    John I. Marden
    Daniel Peña
    Javier Prieto
    Jim O. Ramsay
    Mariano J. Valderrama
    Ana M. Aguilera
    N. Locantore
    J. S. Marron
    D. G. Simpson
    N. Tripoli
    J. T. Zhang
    K. L. Cohen
    Test, 1999, 8 (1) : 1 - 73
  • [19] Robust Principal Component Functional Logistic Regression
    Denhere, Melody
    Billor, Nedret
    COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, 2016, 45 (01) : 264 - 281
  • [20] REGRESSION BASED PRINCIPAL COMPONENT ANALYSIS FOR SPARSE FUNCTIONAL DATA WITH APPLICATIONS TO SCREENING GROWTH PATHS
    Zhang, Wenfei
    Wei, Ying
    ANNALS OF APPLIED STATISTICS, 2015, 9 (02): : 597 - 620