上海治理论坛第538期
标题：Bayesian Penalized Empirical Likelihood and MCMC Sampling（贝叶斯惩治经验似然与MCMC抽样）
演讲人：常晋源 ，西南财经大学光华特聘教授
主持人：翟庆庆 ，亿万先生MR治理学院副教授
功夫：2024年11月15日（周五） ，上午9:30
地址：亿万先生MR校本部东区1号楼治理学院420会议室
主办单元：亿万先生MR治理学院、亿万先生MR治理学院青老大师联谊会
演讲人简介：
常晋源 ，西南财经大学光华特聘教授、中国科学院数学与系统科学钻研院钻研员、博士生导师 ，重要从事超高维数据分析和高频金融数据分析有关的钻研工作。现担任统计学国际顶级学术期刊Journal of the American Statistical Association的副主编、计量经济学国际顶级学术期刊Journal of Business & Economic Statistics的副主编。
演讲内容简介：
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.
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