Empirical Likelihood for Generalized Linear Models with Longitudinal Data

Changming Yin; Mingyao Ai; Xia Chen; Xiangshun Kong

doi:10.1007/s11424-023-2022-2

Empirical Likelihood for Generalized Linear Models with Longitudinal Data

Changming Yin, Mingyao Ai^*, Xia Chen, Xiangshun Kong

^*Corresponding author for this work

School of Mathematics and Statistics

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

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.

Original language	English
Pages (from-to)	2100-2124
Number of pages	25
Journal	Journal of Systems Science and Complexity
Volume	36
Issue number	5
DOIs	https://doi.org/10.1007/s11424-023-2022-2
Publication status	Published - Oct 2023

Keywords

Empirical likelihood ratio
generalized linear model
longitudinal data
maximum empirical likelihood estimator

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10.1007/s11424-023-2022-2

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Yin, C., Ai, M., Chen, X., & Kong, X. (2023). Empirical Likelihood for Generalized Linear Models with Longitudinal Data. Journal of Systems Science and Complexity, 36(5), 2100-2124. https://doi.org/10.1007/s11424-023-2022-2

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title = "Empirical Likelihood for Generalized Linear Models with Longitudinal Data",

abstract = "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.",

keywords = "Empirical likelihood ratio, generalized linear model, longitudinal data, maximum empirical likelihood estimator",

author = "Changming Yin and Mingyao Ai and Xia Chen and Xiangshun Kong",

note = "Publisher Copyright: {\textcopyright} 2023, The Editorial Office of JSSC & Springer-Verlag GmbH Germany.",

year = "2023",

month = oct,

doi = "10.1007/s11424-023-2022-2",

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T1 - Empirical Likelihood for Generalized Linear Models with Longitudinal Data

AU - Yin, Changming

AU - Ai, Mingyao

AU - Chen, Xia

AU - Kong, Xiangshun

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

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U2 - 10.1007/s11424-023-2022-2

DO - 10.1007/s11424-023-2022-2

M3 - Article

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VL - 36

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JO - Journal of Systems Science and Complexity

JF - Journal of Systems Science and Complexity

IS - 5

ER -

Empirical Likelihood for Generalized Linear Models with Longitudinal Data

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