金沙第一娱乐娱城官网_安全购彩

金沙第一娱乐娱城官网
【预告】“百脉大讲坛”第66讲系列:应用概率统计Workshop
时间:2019-11-12

活动时间:11月16日9:00-11:00

活动地点:统计学院资料室


报告(一):A Unified Data-adaptive Framework for High Dimensional Change Point Detection

主讲人:张新生教授

报告摘要:In recent years, change point detection for high dimensional data sequence has become increasingly important in many scientific fields such as biology and finance. The existing literature develops a variety of methods designed for either a specified parameter (e.g. mean or covariance) or a particular alternative pattern (sparse or dense), but not for both scenarios simultaneously. To overcome this limitation, we provide a general framework for developing tests suitable for a large class of parameters, and also adaptive to various alternative scenarios. In particular, by generalizing the classical cumulative sum (CUSUM) statistic, we construct U-statistic based the CUSUM matrix C. Two cases corresponding to common or different change point locations across the components are considered. We then propose two types of individual test statistics by aggregating C based on the adjusted Lp-norm with p ∈ {1, · · · , ∞}. Combining the corresponding individual tests, we construct two types of data-adaptive tests for the two cases, which are both powerful under various alternative patterns. A multiplier bootstrap method is introduced for approximating the proposed test statistics’ limiting distributions. With flexible dependence structure across coordinates and mild moment conditions, we show the optimality of our methods theoretically in terms of size and power by allowing the dimension d and the number of parameters q being much larger than the sample size n. Extensive simulation studies provide further support for our theory. An application to the S&P 100 dataset also demonstrates the usefulness of our proposed methods.  [This is joint work with Bin Liu, Cheng Zhou and Yufeng Liu]

报告人简介:张新生,复旦大学管理学院  教授、博士生导师,统计学系系主任。中国概率统计学会常务理事。主要研究方向为:随机过程及其应用、过程统计、高维数据及大数据的统计推断等。


报告(二):Large dimensional empirical likelihood

主讲人:周望教授

报告摘要:By adding two pseudo-observations to the original data set, we establish the asymptotic normality of the log empirical likelihood ratio statistic when the dimension of the distribution is proportional to the sample size.

报告人简介:周望,新加坡国立大学统计与应用概率系教授。主要从事统计学的理论与应用研究,在高维数据估计、高维数据检验、数据降维、大维数据随机矩阵领域取得了重要的成果。迄今为止,在Annals of Statistics, Journal of American Statistical Association, Journal of Royal Statistical Society(B), Biometrika, Bernoulli, Journal of Econometrics,Trans. Amer. Math. Soc.,Annals of Probability,Annals of Applied Probability等国际顶级期刊发表论文近60篇。

 

金沙第一娱乐娱城官网
Baidu
sogou