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.
| Original language | English |
|---|---|
| Journal | Statistics in Medicine |
| Volume | 35 |
| Issue number | 1 |
| Pages (from-to) | 41-52 |
| Number of pages | 12 |
| ISSN | 0277-6715 |
| DOIs | |
| Publication status | Published - 15 Jan 2016 |