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

TY - JOUR

T1 - Semi-parametric estimation of random effects in a logistic regression model using conditional inference

AU - Petersen, Jørgen Holm

N1 - Copyright © 2015 John Wiley & Sons, Ltd.

PY - 2016/1/15

Y1 - 2016/1/15

N2 - 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.

AB - 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.

U2 - 10.1002/sim.6611

DO - 10.1002/sim.6611

M3 - Journal article

C2 - 26265116

SN - 0277-6715

VL - 35

SP - 41

EP - 52

JO - Statistics in Medicine

JF - Statistics in Medicine

IS - 1

ER -