Second-order Least Squares Estimation in Generalized Linear Mixed Models

Second-order Least Squares Estimation in Generalized Linear Mixed Models

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Maximum likelihood is an ubiquitous method used in the estimation of generalized linear mixed model (GLMM). However, the method entails computational difficulties and relies on the normality assumption for random effects. We propose a second-order least squares (SLS) estimator based on the first two...

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Bibliographic Details
Main Author: Li, He
Other Authors: Wang, Liqun(Statistics)
Language:en_US
Published: 2011
Subjects:
Bias reduction
Discrete response
Influence function
Instrumental variable
Least squares method
Longitudinal data
Measurement error
M-estimator
Mixed effects models
Outliers
Robustness
Simulation-based estimator
Online Access:http://hdl.handle.net/1993/4446

Internet

http://hdl.handle.net/1993/4446

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