Semi‐parametric estimation of random effects in a logistic regression model using conditional inference | Zendy

Petersen Jørgen Holm | Zendy

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Semi‐parametric estimation of random effects in a logistic regression model using conditional inference

Author(s) -

Petersen Jørgen Holm

Publication year - 2015

Publication title -

statistics in medicine

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.996

H-Index - 183

eISSN - 1097-0258

pISSN - 0277-6715

DOI - 10.1002/sim.6611

Subject(s) - statistics , logistic regression , conditional variance , estimator , restricted maximum likelihood , random effects model , inference , mathematics , econometrics , parametric statistics , estimation , conditional probability distribution , computer science , maximum likelihood , artificial intelligence , autoregressive conditional heteroskedasticity , medicine , volatility (finance) , meta analysis , management , economics

This paper describes a new approach to the estimation in a logistic regression model with two crossed random effects where special interest is in estimating the variance of one of the effects while not making distributional assumptions about the other effect. A composite likelihood is studied. For each term in the composite likelihood, a conditional likelihood is used that eliminates the influence of the random effects, which results in a composite conditional likelihood consisting of only one‐dimensional integrals that may be solved numerically. Good properties of the resulting estimator are described in a small simulation study. Copyright © 2015 John Wiley & Sons, Ltd.

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