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

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 l...

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Bibliographic Details
Published inJournal of systems science and complexity Vol. 31; no. 5; pp. 1362 - 1376
Main Authors Gao, Qibing, Du, Xiuli, Zhou, Xiuqing, Xie, Fengchang
Format Journal Article
LanguageEnglish
Published Beijing Academy of Mathematics and Systems Science, Chinese Academy of Sciences 01.10.2018
Springer Nature B.V
Subjects
Asymptotic properties
Complex Systems
Computer simulation
Control
Convergence
Economic models
Estimating techniques
Estimators
Generalized linear models
Mathematics
Mathematics and Statistics
Mathematics of Computing
Monte Carlo simulation
Normality
Operations Research/Decision Theory
Statistical models
Statistical tests
Statistics
Systems Theory
generalized linear models
diverging dimension
linear hypothesis
Asymptotic normality
maximum quasi-likelihood estimators
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Summary: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.
ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-018-7017-z