Second-order Least Squares Estimation in Generalized Linear Mixed Models
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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| Language: | en_US |
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2011
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| Online Access: | http://hdl.handle.net/1993/4446 |