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Asymptotic Properties of Maximum Quasi-Likelihood Estimators in Generalized Linear Models with Diverging Number of Covariates

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Abstract

In this paper, for the generalized linear models (GLMs) with diverging number of covariates, the asymptotic properties of maximum quasi-likelihood estimators (MQLEs) under some regular conditions are developed. The existence, weak convergence and the rate of convergence and asymptotic normality of linear combination of MQLEs and asymptotic distribution of single linear hypothesis test statistics are presented. The results are illustrated by Monte-Carlo simulations.

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Authors and Affiliations

  1. School of Mathematics Science, Nanjing Normal University, Nanjing, 210023, China

    Qibing Gao, Xiuli Du, Xiuqing Zhou & Fengchang Xie

Authors
  1. Qibing Gao
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  2. Xiuli Du
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  3. Xiuqing Zhou
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  4. Fengchang Xie
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Corresponding author

Correspondence to Qibing Gao.

Additional information

This paper was supported by Major Programm of Natural Science Foundation of China under Grant No. 71690242, the Natural Science Foundation of China under Grant No. 11471252, and the National Social Science Fund of China under Grant No. 18BTJ040.

This paper was recommended for publication by Editor DONG Yuexiao.

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Gao, Q., Du, X., Zhou, X. et al. Asymptotic Properties of Maximum Quasi-Likelihood Estimators in Generalized Linear Models with Diverging Number of Covariates. J Syst Sci Complex 31, 1362–1376 (2018). https://doi.org/10.1007/s11424-018-7017-z

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  • DOI: https://doi.org/10.1007/s11424-018-7017-z

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