TY - JOUR
T1 - Empirical Likelihood for Generalized Linear Models with Longitudinal Data
AU - Yin, Changming
AU - Ai, Mingyao
AU - Chen, Xia
AU - Kong, Xiangshun
N1 - Publisher Copyright:
© 2023, The Editorial Office of JSSC & Springer-Verlag GmbH Germany.
PY - 2023/10
Y1 - 2023/10
N2 - Generalized linear models are usually adopted to model the discrete or nonnegative responses. In this paper, empirical likelihood inference for fixed design generalized linear models with longitudinal data is investigated. Under some mild conditions, the consistency and asymptotic normality of the maximum empirical likelihood estimator are established, and the asymptotic χ2 distribution of the empirical log-likelihood ratio is also obtained. Compared with the existing results, the new conditions are more weak and easy to verify. Some simulations are presented to illustrate these asymptotic properties.
AB - Generalized linear models are usually adopted to model the discrete or nonnegative responses. In this paper, empirical likelihood inference for fixed design generalized linear models with longitudinal data is investigated. Under some mild conditions, the consistency and asymptotic normality of the maximum empirical likelihood estimator are established, and the asymptotic χ2 distribution of the empirical log-likelihood ratio is also obtained. Compared with the existing results, the new conditions are more weak and easy to verify. Some simulations are presented to illustrate these asymptotic properties.
KW - Empirical likelihood ratio
KW - generalized linear model
KW - longitudinal data
KW - maximum empirical likelihood estimator
UR - http://www.scopus.com/inward/record.url?scp=85148377527&partnerID=8YFLogxK
U2 - 10.1007/s11424-023-2022-2
DO - 10.1007/s11424-023-2022-2
M3 - Article
AN - SCOPUS:85148377527
SN - 1009-6124
VL - 36
SP - 2100
EP - 2124
JO - Journal of Systems Science and Complexity
JF - Journal of Systems Science and Complexity
IS - 5
ER -