Performance of likelihood-based estimation methods for multilevel binary regression models.

Abstract

By means of a fractional factorial simulation experiment, we compare the performance of Penalised Quasi-Likelihood, Non-Adaptive Gaussian Quadrature and Adaptive Gaussian Quadrature in estimating parameters for multi-level logistic regression models. The comparison is done in terms of bias, mean squared error, numerical convergence, and computational efficiency. It turns out that, in terms of Mean Squared Error, standard versions of the Quadrature methods perform relatively poor in comparison with Penalized Quasi-Likelihood.Bias; Binary regression; Convergence; Efficiency; Factorial; Fractional factorial experiment; Gaussian quadrature; Logistic regression; Methods; Model; Models; Monte Carlo simulation; Multilevel analysis; Penalised quasi-likelihood; Performance; Regression; Simulation;