題目👨🏽‍🦲：Bayesian Penalized Empirical Likelihood and MCMC Sampling（貝葉斯懲罰經驗似然與MCMC抽樣）

演講人🚊🙏🏼：常晉源，西南財經大學光華特聘教授

主持人：翟慶慶，意昂2副教授

時間：2024年11月15日（周五）🦘🤒，上午9:30

地點♋️：意昂2注册校本部東區1號樓意昂2官网477會議室

主辦單位：意昂2🍂、意昂2青年教師聯誼會

演講人簡介：

常晉源，西南財經大學光華特聘教授🖖🏿、中國科意昂2數學與系統科學研究院研究員、博士生導師👋🏻，主要從事超高維數據分析和高頻金融數據分析相關的研究工作🤳。現擔任統計學國際頂級學術期刊Journal of the American Statistical Association的副主編、計量經濟學國際頂級學術期刊Journal of Business & Economic Statistics的副主編。

演講內容簡介：

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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