General unbiased estimating equations for variance components in linear mixed models

This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used estimators such as the restricted maximum likelihood estimator. We derive the asymptotic covariance matrices and second-orde...

Full description

Saved in:
Bibliographic Details
Published in:Japanese journal of statistics and data science 2021-12, Vol.4 (2), p.841-859
Main Authors: Kubokawa, T., Sugasawa, S., Tamae, H., Chaudhuri, S.
Format: Article
Language:English
Subjects:
Chemistry and Earth Sciences
Computer Science
Economics
Finance
Health Sciences
Humanities
Insurance
Law
Management
Mathematics and Statistics
Medicine
Original Paper
Physics
Statistical Theory and Methods
Statistics
Statistics and Computing/Statistics Programs
Statistics for Business
Statistics for Engineering
Statistics for Life Sciences
Statistics for Social Sciences
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper introduces a general framework for estimating variance components in the linear mixed models via general unbiased estimating equations, which include some well-used estimators such as the restricted maximum likelihood estimator. We derive the asymptotic covariance matrices and second-order biases under general estimating equations without assuming the normality of the underlying distributions and identify a class of second-order unbiased estimators of variance components. It is also shown that the asymptotic covariance matrices and second-order biases do not depend on whether the regression coefficients are estimated by the generalized or ordinary least squares methods. We carry out numerical studies to check the performance of the proposed methods based on typical linear mixed models.
ISSN:2520-8756
2520-8764
DOI:10.1007/s42081-021-00138-8