Artículos de revistas
Prediction in Multilevel Logistic Regression
COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.39, n.6, p.1083-1096, 2010
TAMURA, Karin Ayumi
The purpose of this article is to present a new method to predict the response variable of an observation in a new cluster for a multilevel logistic regression. The central idea is based on the empirical best estimator for the random effect. Two estimation methods for multilevel model are compared: penalized quasi-likelihood and Gauss-Hermite quadrature. The performance measures for the prediction of the probability for a new cluster observation of the multilevel logistic model in comparison with the usual logistic model are examined through simulations and an application.