Maximum Likelihood Variance Components Estimation for Binary Data

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 Gibb... Ausführliche Beschreibung

1. Person: McCulloch, Charles E.
Quelle: in Journal of the American Statistical Association : JASA : the premier journal of statistical science Vol. 89, No. 425 (1994), p. 330-335
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Format: Online-Artikel
Sprache: English
Veröffentlicht: 1994
Beschreibung: Online-Ressource
Schlagworte: research-article
EM algorithm
Fixed and random effects
Generalized linear models
Monte Carlo Markov chain
Probit
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Anmerkung: Copyright: Copyright 1994 American Statistical Association
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520 |a 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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653 |a EM algorithm 
653 |a Fixed and random effects 
653 |a Generalized linear models 
653 |a Monte Carlo Markov chain 
653 |a Probit 
773 0 8 |i in  |t Journal of the American Statistical Association : JASA : the premier journal of statistical science  |d Abingdon : Taylor & Francis  |g Vol. 89, No. 425 (1994), p. 330-335  |q 89:425<330-335  |w (DE-601)JST043979041  |x 0162-1459 
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952 |d 89  |j 1994  |e 425  |h 330-335 
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