標(biāo)題：Exploring Excellence: Bayesian Penalized Empirical Likelihood and MCMC Sampling
報(bào)告時(shí)間：2024年03月15日（星期五）16:00-17:00
報(bào)告地點(diǎn)：人民大街校區(qū)數(shù)學(xué)與統(tǒng)計(jì)學(xué)院415室
主講人：常晉源
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院
報(bào)告內(nèi)容簡(jiǎn)介：
In this study, we introduce a novel methodological framework known as Bayesian penalized empirical likelihood, designed to tackle the computational challenges associated with empirical likelihood methods. Our approach pursues two primary objectives: firstly, preserving the inherent flexibility of empirical likelihood to accommodate a wide range of model conditions, and secondly, providing convenient access to well-established Markov chain Monte Carlo (MCMC) sampling schemes. To achieve the first objective, we propose a penalized approach that effectively selects model conditions by regulating Lagrange multipliers, thereby reducing the dimensionality of the problem while leveraging a comprehensive set of model conditions. For the second objective, our approach overcomes the obstacles inherent in devising sampling schemes for Bayesian applications through efficient dimensionality reduction. Our Bayesian penalized empirical likelihood framework offers a flexible and efficient approach, enhancing the adaptability and practicality of empirical likelihood methods in statistical inference. Furthermore, our study illustrates the practical advantages of utilizing sampling techniques over optimization methods, as they exhibit rapid convergence to global optima of posterior distributions, ensuring robust parameter estimation. This framework provides a valuable tool for researchers and analysts grappling with complex problems.
主講人簡(jiǎn)介：
常晉源，西南財(cái)經(jīng)大學(xué)光華特聘教授、中科院數(shù)學(xué)與系統(tǒng)科學(xué)研究院研究員、博士生導(dǎo)師，主要從事“超高維數(shù)據(jù)分析”和“高頻金融數(shù)據(jù)分析”兩個(gè)領(lǐng)域的研究，獲國(guó)家級(jí)高層次領(lǐng)軍人才資助，先后擔(dān)任JRSSB、Statistica Sinica、JBES和JASA的副主編。
