TY - JOUR
T1 - Penalized generalized estimating equations approach to longitudinal data with multinomial responses
AU - Kamruzzaman, Md
AU - Kwon, Oran
AU - Park, Taesung
N1 - Funding Information:
This research was supported by a grant of the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (Grant number: HI16C2037), and the Bio & Medical Technology Development Program of the National Research Foundation of Korea (NRF) (Grant number: 2013M3A9C4078158).
Publisher Copyright:
© 2021, Korean Statistical Society.
PY - 2021/9
Y1 - 2021/9
N2 - In high-dimensional longitudinal data with multinomial response, the number of covariates is always much larger than the number of subjects and when modelling such data, variable selection is always an important issue. In this study, we developed the penalized generalized estimating equation for multinomial responses for identifying important variables and estimation of their regression coefficients simultaneously. An iterative algorithm is used to solve the penalized estimating equation by combining the Fisher-scoring algorithm and minorization-maximization algorithm. We used a penalty term to regularize the slope part only because category-specific intercept terms should be included in the multinomial model. We conducted a simulation study to investigate the performance of the proposed method and demonstrated its performance using real dataset.
AB - In high-dimensional longitudinal data with multinomial response, the number of covariates is always much larger than the number of subjects and when modelling such data, variable selection is always an important issue. In this study, we developed the penalized generalized estimating equation for multinomial responses for identifying important variables and estimation of their regression coefficients simultaneously. An iterative algorithm is used to solve the penalized estimating equation by combining the Fisher-scoring algorithm and minorization-maximization algorithm. We used a penalty term to regularize the slope part only because category-specific intercept terms should be included in the multinomial model. We conducted a simulation study to investigate the performance of the proposed method and demonstrated its performance using real dataset.
KW - High-dimensional data
KW - Longitudinal data
KW - Minimax Concave Penalty
KW - Minorization-maximization algorithm
KW - Multinomial response
KW - Smoothly Clipped Absolute Deviation penalty
KW - Variable selection
UR - http://www.scopus.com/inward/record.url?scp=85110840337&partnerID=8YFLogxK
U2 - 10.1007/s42952-021-00134-4
DO - 10.1007/s42952-021-00134-4
M3 - Article
AN - SCOPUS:85110840337
SN - 1226-3192
VL - 50
SP - 844
EP - 859
JO - Journal of the Korean Statistical Society
JF - Journal of the Korean Statistical Society
IS - 3
ER -