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Performance of variance estimators in the analysis of longitudinal data with a large cluster size
Hu, Shuwen Wang, You-Gan Fu, Liya
Hu, Shuwen
Wang, You-Gan
Fu, Liya
Author
Abstract
Generalized estimating equations (GEE) is a widely used method for analysing longitudinal data, and the sandwich method is often used to estimate the variance–covariance matrix of the regression coefficient estimators. However, the sandwich method relies on the residual products as an estimator for the true covariance of the responses, and the estimator becomes singular and sparse when the cluster size (n) becomes larger. We carry out a large set of simulation studies to address the question, when the cluster size becomes large, whether the sandwich estimator and their modified versions are still valid when a sparse or singular matrix is used as an estimate of the true covariance of the responses. The performance of smooth bootstrap which involves perturbing the estimating functions is also worth to be investigated when the cluster size is large. Two datasets are analysed to illustrate these findings.
Keywords
covariance estimate, large cluster size, generalized estimating equations, smooth bootstrap, regularized sandwich estimate
Date
2022
Type
Journal article
Journal
Journal of Statistical Computation and Simulation
Book
Volume
92
Issue
1
Page Range
1-18
Article Number
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
License
All rights reserved
File Access
Controlled