Bayesian Inference for Finite Mixture Regression Model Based on Non-Iterative Algorithm

Ang Shan and Fengkai Yang
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Ang Shan: School of Mathematics, Shandong University, Jinan 250100, China
Fengkai Yang: School of Mathematics and Statistics, Shandong University, Weihai 264209, China

Mathematics, 2021, vol. 9, issue 6, 1-13

Abstract: Finite mixtures normal regression (FMNR) models are widely used to investigate the relationship between a response variable and a set of explanatory variables from several unknown latent homogeneous groups. However, the classical EM algorithm and Gibbs sampling to deal with this model have several weak points. In this paper, a non-iterative sampling algorithm for fitting FMNR model is proposed from a Bayesian perspective. The procedure can generate independently and identically distributed samples from the posterior distributions of the parameters and produce more reliable estimations than the EM algorithm and Gibbs sampling. Simulation studies are conducted to illustrate the performance of the algorithm with supporting results. Finally, a real data is analyzed to show the usefulness of the methodology.

Keywords: finite mixture regression; non-iterative sampling; missing data; Gibbs sampling; EM algorithm (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
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