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

Shiyi Tang and Jiali Zheng

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 17, 5971-5992

Abstract: 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.

Date: 2022
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DOI: 10.1080/03610926.2020.1851717

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