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Semi-parametric estimation of random effects in a logistic regression model using conditional inference.
- Source :
- Statistics in Medicine; 1/15/2016, Vol. 35 Issue 1, p41-52, 12p
- Publication Year :
- 2016
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 02776715
- Volume :
- 35
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Statistics in Medicine
- Publication Type :
- Academic Journal
- Accession number :
- 112037649
- Full Text :
- https://doi.org/10.1002/sim.6611