A Cross-Entropy Approach to the Estimation of Generalized Linear Multilevel Models
本文提出用交叉熵方法拟合广义线性多层模型的最大似然估计，改进了计算性能，并通过蒙特卡洛实验确定最优参数，在统计和计算上均优于传统数值积分方法。
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.