Work

Variance-reduced sampling importance resampling

Year: 2024

Type: article

Abstract: The sampling importance resampling method is widely utilized in various fields, such as numerical integration and statistical simulation. In this paper, two modified methods are presented by incorporating two variance reduction techniques commonly used in Monte Carlo simulation, namely antithetic sampling and Latin hypercube sampling, into the process of sampling importance resampling method, respectively. Theoretical evidence is provided to demonstrate that the proposed methods significantly reduce estimation errors compared to the original approach. Furthermore, the effectiveness and advantages of the proposed methods are validated through both numerical studies and real data analysis.

Source: Communications in Statistics - Simulation and Computation

DOI: 10.1080/03610918.2024.2391897

Authors: Yao Xiao, Kang Fu, Kun Li

Institutions: Beijing Institute of Technology, Central China Normal University, Zhongnan University of Economics and Law

Language: English

Topic: Markov Chains and Monte Carlo Methods

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Ministry of Education of the People's Republic of China, Humanities and Social Sciences Youth Foundation, Ministry of Education of the People's Republic of China, Science Foundation of Ministry of Education of China

Awards: G1069496180, G5022088892, G6158637224