Maximum Likelihood Variance Components Estimation for Binary Data 论文

1994Journal of the American Statistical Association引用 219
DOI
Statistical Methods and Bayesian InferenceBayesian Methods and Mixture ModelsSoil Geostatistics and Mapping
Statistical Methods and Bayesian InferenceBayesian Methods and Mixture ModelsSoil Geostatistics and Mapping
相关技术:Bayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference
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Abstract We consider a class of probit-normal models for binary data and describe ML and REML estimation of variance components for that class as well as best prediction for the realized values of the random effects. ML estimates are calculated using an EM algorithm; for complicated models EM includes a Gibbs step. The computations are illustrated through two examples.

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Charles E. McCulloch

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Bayesian Methods and Mixture ModelsStatistical Methods and Bayesian Inference

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