拉斯维加斯9888

（一）、

Title（标题）：On estimation of the population spectrum from large dimensional

covariance matrices

Speaker（汇报人）：Prof. Jianfeng Yao, The University of Hong Kong

Time（功夫）：2012年5月17日（周四）下午02:00-3:00

Place（地址）：拉斯维加斯9888新楼217教室

Abstract（提要）：For large-dimensional data, sample covariance matrices significantly deviate from the population covariance matrix. For various inference problem, it is then crucial to recover population characteristics, e.g. distribution of eigenvalues of the population covariance matrix from the sample covariance matrices. First we will give a review of existing methods for estimation of this distribution. Then recent advances on this topic using contour-integral based methods or extended Stieldjes transform will be presented. In particular advantages and weakness of these methods will be discussed and compared.In the context of time series, these methods can be applied to series of returns that are widely accepted as uncorrelated in time. The discussed methods are therefore intended to a analysis of the correlation structure within different stock prices.

（二）、

Title（标题）：An Introduction to Bayesian Analysis of Hierarchical Models

Speaker（汇报人）：Prof. George C. Tiao, The University of Chicago、北京大台甫望教授

Time（功夫）：2012年5月17日（周四）下午03:30-4:30

Place（地址）：拉斯维加斯9888新楼217教室

Abstract（提要）：Hierarchical models are widely used in statistical application, and their analyses are particularly suited for the Bayesian approach. Their increasing popularity in recent years, especially in Marketing research, is the result of phenomenal growth in computer storage and powerful computing techniques. In this report, I shall briefly go over the following topics: 1, Approaches to statistical inference: frequentist vs. Bayes; 2. MCMC computing methods; 3, Hierarchical models: fixed effect and random effects; 4, Application to atmospheric data trend analysis, and to marketing and other data; and 5, Dimension reduction, random effect models and Bayesian analysis in model building.

注：3:00——3:30为茶歇功夫，为各人筹备了茶点。