A Cross-Entropy Approach to the Estimation of Generalized Linear Multilevel Models. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Bee, Marco; Espa, Giuseppe; Giuliani, Diego; Santi, Flavio
• Is Part Of: Journal of computational and graphical statistics. Volume 26:Issue 3(2017)
• Page Start: 695
• Page End: 708
• Publication Date: 2017-07-03
• Subjects: Adaptive Gaussian quadrature -- Clustering effects -- Maximum likelihood estimation -- Multilevel logit; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2016.1278003
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: In this article, we use the cross-entropy method for noisy optimization for fitting generalized linear multilevel models through maximum likelihood. We propose specifications of the instrumental distributions for positive and bounded parameters that improve the computational performance. We also introduce a new stopping criterion, which has the advantage of being problem-independent. In a second step we find, by means of extensive Monte Carlo experiments, the most suitable values of the input parameters of the algorithm. Finally, we compare the method to the benchmark estimation technique based on numerical integration. The cross-entropy approach turns out to be preferable from both the statistical and the computational point of view. In the last part of the article, the method is used to model the probability of firm exits in the healthcare industry in Italy. Supplemental materials are available online.