rfmm: a Stata command for the minimum density power divergence estimation of finite mixtures of regression models

The minimum density power divergence (MDPD) framework (Basu et al., 1998) provides a family of estimators indexed by a parameter (α), which controls the tradeoff between efficiency and robustness. In this paper, we extend this estimation framework to finite mixtures of regression models. In order to make this extension readily accessible to researchers, we provide the new Stata command rfmm, which allows for the MDPD estimation of finite mixtures of Gaussian, Poisson, and negative binomial regression models. Of special note is that the proposed command provides a graphical tool for preliminary diagnostics on the appropriate number of mixtures’ components based on the L2 criterion function (Scott, 2009). We compare the performance of the MDPD family of estimators provided by rfmm with the ML estimator via Monte Carlo simulations for correctly specified and gross-error contaminated mixture of Poisson regression models. Finally, the proposed package is illustrated using applications from the biometrical and health economics literatures.