Small Area Estimation under Poisson–Dirichlet Process Mixture Models

Author:

Qiu Xiang1ORCID,Ke Qinchun1ORCID,Zhou Xueqin1ORCID,Liu Yulu12ORCID

Affiliation:

1. School of Science, Shanghai Institude of Technology, Shanghai 201418, China

2. Shanghai Institute of Applied Mathematics and Mechanics, School of Mechanics and Engineering Science, Shanghai University, Shanghai 200072, China

Abstract

In this paper, we propose an improved Nested Error Regression model in which the random effects for each area are given a prior distribution using the Poisson–Dirichlet Process. Based on this model, we mainly investigate the construction of the parameter estimation using the Empirical Bayesian(EB) estimation method, and we adopt various methods such as the Maximum Likelihood Estimation(MLE) method and the Markov chain Monte Carlo algorithm to solve the model parameter estimation jointly. The viability of the model is verified using numerical simulation, and the proposed model is applied to an actual small area estimation problem. Compared to the conventional normal random effects linear model, the proposed model is more accurate for the estimation of complex real-world application data, which makes it suitable for a broader range of application contexts.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

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