Penalized estimation in finite mixture of ultra-high dimensional regression models

In this paper, we propose a penalized estimation method for finite mixture of ultra-high dimensional regression models. A two-step procedure is explored. Firstly, we conduct order selection with the number of components unknown. Then variable selection is applied to ultra-high dimensional regression models. A specific EM algorithm is designed to maximize penalized log-likelihood function. We demonstrate our method by numerical simulations which performs well. Further, an empirical study of return on equity (ROE) prediction is shown to consolidate our methodology.

Automated summary

This paper proposes a penalized estimation method for finite mixtures of ultra-high dimensional regression models. A two-step procedure is used for order selection and variables selection, with a specific EM algorithm maximizing the penalized log-likelihood function. Numerical simulations and an empirical study validate the effectiveness of the method.