Dispersion parameter extension of precise generalized linear mixed model asymptotics

Aishwarya Bhaskaran; Matt P. Wand

doi:10.1016/j.spl.2022.109691

Dispersion parameter extension of precise generalized linear mixed model asymptotics

Aishwarya Bhaskaran^*, Matt P. Wand

^*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

We extend a recent asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The maximum likelihood estimators of all model parameters have asymptotically normal distributions with asymptotic mutual independence between fixed effects, covariance and dispersion parameters.

Original language	English
Article number	109691
Pages (from-to)	1-8
Number of pages	8
Journal	Statistics and Probability Letters
Volume	193
DOIs	https://doi.org/10.1016/j.spl.2022.109691
Publication status	Published - Feb 2023
Externally published	Yes

Keywords

Longitudinal data analysis
Maximum likelihood estimation
Multilevel models
Studentization

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10.1016/j.spl.2022.109691

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title = "Dispersion parameter extension of precise generalized linear mixed model asymptotics",

abstract = "We extend a recent asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The maximum likelihood estimators of all model parameters have asymptotically normal distributions with asymptotic mutual independence between fixed effects, covariance and dispersion parameters.",

keywords = "Longitudinal data analysis, Maximum likelihood estimation, Multilevel models, Studentization",

author = "Aishwarya Bhaskaran and Wand, {Matt P.}",

year = "2023",

month = feb,

doi = "10.1016/j.spl.2022.109691",

language = "English",

volume = "193",

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journal = "Statistics and Probability Letters",

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T1 - Dispersion parameter extension of precise generalized linear mixed model asymptotics

AU - Bhaskaran, Aishwarya

AU - Wand, Matt P.

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N2 - We extend a recent asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The maximum likelihood estimators of all model parameters have asymptotically normal distributions with asymptotic mutual independence between fixed effects, covariance and dispersion parameters.

AB - We extend a recent asymptotic normality theorem for generalized linear mixed models to include the dispersion parameter. The maximum likelihood estimators of all model parameters have asymptotically normal distributions with asymptotic mutual independence between fixed effects, covariance and dispersion parameters.

KW - Longitudinal data analysis

KW - Maximum likelihood estimation

KW - Multilevel models

KW - Studentization

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U2 - 10.1016/j.spl.2022.109691

DO - 10.1016/j.spl.2022.109691

M3 - Article

AN - SCOPUS:85140453169

SN - 0167-7152

VL - 193

SP - 1

EP - 8

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

M1 - 109691

ER -

Dispersion parameter extension of precise generalized linear mixed model asymptotics

Abstract

Keywords

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