Semi-parametric estimation of random effects in a logistic regression model using conditional inference.
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.
Department of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
Jørgen Holm
JH
Petersen
D056808:Biostatistics / Q000379:methods; D003198:Computer Simulation; D005260:Female; D006801:Humans; D007231:Infant, Newborn; D017078:Laser-Doppler Flowmetry / Q000706:statistics & numerical data; D016013:Likelihood Functions; D016015:Logistic Models; D015233:Models, Statistical; D054238:Perinatal Mortality; D011247:Pregnancy; D016216:Ultrasonography, Prenatal / Q000706:statistics & numerical data; D014469:Umbilical Arteries / Q000000981:diagnostic imaging
composite likelihood; estimation; logistic regression; random effects; rater agreement