Recently, the paper “Bayesian penalized empirical likelihood and MCMC sampling”, jointly completed by Professor Jinyuan Chang, Professor Chengyong Tang at Temple University in the United States and postdoctoral researcher Yanzheng Zhu has been officially accepted by Journal of the American Statistical Association.
Abstract
In this study, we introduce a novel methodological framework called Bayesian Penalized Empirical Likelihood (BPEL), designed to address the computational challenges inherent in empirical likelihood (EL) approaches. Our approach has two primary objectives: (i) to enhance the inherent flexibility of EL in accommodating diverse model conditions, and (ii) to facilitate the use of well-established Markov Chain Monte Carlo (MCMC) sampling schemes as a convenient alternative to the complex optimization typically required for statistical inference using EL. To achieve the first objective, we propose a penalized approach that regularizes the Lagrange multipliers, significantly reducing the dimensionality of the problem while accommodating a comprehensive set of model conditions. For the second objective, our study designs and thoroughly investigates two popular sampling schemes within the BPEL context. We demonstrate that the BPEL framework is highly flexible and efficient, enhancing the adaptability and practicality of EL methods. Our study highlights the practical advantages of using sampling techniques over traditional optimization methods for EL problems, showing rapid convergence to the global optima of posterior distributions and ensuring the effective resolution of complex statistical inference challenges.
Author Introduction
Jinyuan Chang, executive director of the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics, Guanghua distinguished professor, and recipient of the National Science Fund for Distinguished Young Scholars of China. Mainly engaged in two research fields: ultra-high dimensional data analysis and high-frequency financial data analysis.
Chengyong Tang, professor at Temple University in the United States, mainly focusing on research in empirical likelihood, longitudinal and correlated data analysis, as well as high-dimensional data analysis.
Yuanzheng Zhu, postdoctoral researcher at the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics, primarily concentrates on ultra-high-dimensional data analysis and Bayesian statistics.
