应云顶国际4008服务平台邀请，巴西INSPER Institute of Education and Research统计与计量经济学系教授Hedibert Freitas Lopes于4月26日-28日来我校进行学术交流并做学术报告。

题 目：Dynamic Sparsity on Dynamic Regression Models

时 间：2018年4月27日（周五），上午9：00～10：00

地 点：云顶国际4008服务平台北五楼427

摘 要：We consider variable selection and shrinkage for the Gaussian dynamic linear regression within a Bayesian framework. In particular, we propose a novel method that allows for time-varying sparsity, based on an extension of spike-and-slab priors for dynamic models. This is done by assigning appropriate Markov switching priors for the time-varying coefficients’ variances, extending the previous work of Ishwaran and Rao (2005). Furthermore, we investigate different priors, including the common Inverted gamma prior for the process variances, and other mixture prior distributions such as Gamma priors for both the spike and the slab, which leads to a mixture of Normal-Gammas priors (Griffin et al., 2010) for the coefficients. In this sense, our prior can be view as a dynamic variable selection prior which induces either smoothness (through the slab) or shrinkage towards zero (through the spike) at each time point.  The MCMC method used for posterior computation uses Markov latent variables that can assume binary regimes at each time point to generate the coefficients’ variances. In that way, our model is a dynamic mixture model, thus, we could use the algorithm of Gerlach et al. (2000) to generate the latent processes without conditioning on the states. Finally, our approach is exemplified through simulated examples and a real data application.  This is joint work with Paloma V. Uribe.

Keywords: Cholesky decomposition, dynamic models, normal-gamma prior, spike-and-slab priors, high-dimensional data, scale mixture of normals.

报告人简介：

Hedibert Freitas Lopes教授在美国Duke University获得统计学博士学位。2003-2013年在美国芝加哥大学Booth商学院计量经济学与统计系任教。目前是巴西INSPER Institute of Education and Research统计与计量经济学系教授。主要从事Bayesian statistics相关领域的研究。在Annals of Applied Statistics、Journal of the American Statistical Association、Journal of Econometrics、Bayesian Analysis、Econometrics and Statistics、Biometrics等国际权威期刊上发表学术论文230多篇。主持美国、巴西自然科学基金项目6项。

欢迎感兴趣的师生参加！