Model selection for the localized mixture of experts models

ABSTRACT

In this paper, we propose a penalized likelihood method to simultaneous select covariate, and mixing component and obtain parameter estimation in the localized mixture of experts models. We develop an expectation maximization algorithm to solve the proposed penalized likelihood procedure, and introduce a data-driven procedure to select the tuning parameters. Extensive numerical studies are carried out to compare the finite sample performances of our proposed method and other existing methods. Finally, we apply the proposed methodology to analyze the Boston housing price data set and the baseball salaries data set.

KEYWORDS:

• Localized mixture of experts models
• EM algorithm
• SCAD penalty function

CLASSIFICATION CODES:

• 62H12
• 62H30

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Jiang's research is partially supported by the National Natural Science Foundation of China [No. 11301221] and the National Statistical Scientific Research Center Projects [No. 2017LY65].