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• W2065745947 abstract "A broad class of generalized linear mixed models, e.g. variance components models for binary data, percentages or count data, will be introduced by incorporating additional random effects into the linear predictor of a generalized linear model structure. Parameters are estimated by a combination of quasi-likelihood and iterated MINQUE (minimum norm quadratic unbiased estimation), the latter being numerically equivalent to REML (restricted, or residual, maximum likelihood). First, conditional upon the additional random effects, observations on a working variable and weights are derived by quasi-likelihood, using iteratively re-weighted least squares. Second, a linear mixed model is fitted to the working variable, employing the weights for the residual error terms, by iterated MINQUE. The latter may be regarded as a least squares procedure applied to squared and product terms of error contrasts derived from the working variable. No full distributional assumptions are needed for estimation. The model may be fitted with standardly available software for weighted regression and REML." @default.
• W2065745947 created "2016-06-24" @default.
• W2065745947 creator A5019484533 @default.
• W2065745947 creator A5067238389 @default.
• W2065745947 date "1994-03-01" @default.
• W2065745947 modified "2023-09-26" @default.
• W2065745947 title "A simple approach for the analysis of generalizea linear mixed models" @default.
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• W2065745947 doi "https://doi.org/10.1111/j.1467-9574.1994.tb01428.x" @default.
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