Simulation-Based Fitting of Intractable Models via Sequential Sampling and Local Smoothing

The recent publication of a comprehensive algorithm for fitting generative models marks a significant advancement in statistical modeling. This algorithm addresses the complexities associated with models whose likelihoods and moments are not easily tractable, providing a solution that requires minimal prior information about model parameters. By integrating a global search phase with a local search phase that utilizes a trust region approach akin to the Fisher scoring algorithm, the method enhances the accuracy of quasi-likelihood estimators. Comparisons with alternative methods have demonstrated its superior performance, underscoring its potential impact on the field. The availability of an R package on CRAN further facilitates its adoption, allowing researchers and practitioners to leverage this innovative approach in their work.