中国人民大学统计学院

---“渊澄取映”

编辑寄语：“渊澄取映”是取自中国古代《千字文》中一句诗，指古人修炼道德要以深潭清澈之水为镜。我们借此古语作为中国人民大学统计学院系列学术讲座的别名，意指聆听名师博大精深的演讲，效仿前辈纯真自然的学养，开启学子年轻灵动的智慧！

为加强统计学国际学术交流，中国人民大学统计学院定于每学期星期三下午15：30 pm-16：50pm（特殊情况除外）在明德主楼1030统计学院学术讲堂举办定期学术演讲。届时我们将邀请到海内外统计学及相关领域的优秀学者来到统计学院学术讲堂，与广大师生分享研究成果，交流学术思想。我们鼓励学生积极参与系列讲座，启迪思路、激扬心灵，见贤思齐、反躬自问。

最新讲座通知（1）

我们邀请到的北卡蒋建成博士将于7月2日下午15:30在我学院1030给学生做学术演讲，我们诚恳地邀请各位老师和学生积极参加！

报告题目：

Weighted nonlinear quantile regression and oracle model selection

摘要:

We suggest a weighted quantile regression estimation approach for nonlinear models with a fixed or diverging number of parameters. The proposed estimation method is applicable to other models. We use the adaptive-LASSO and SCAD regularization to select variables/parameters in the models. Under weak conditions, we establish asymptotic distributions of the proposed estimators, which show that the variable/parameter selection methods perform as well as if the correct submodels are known in advance. We also suggest an algorithm for fast implementation of the proposed methodology. Simulations are conducted to compare different estimators.
A real example is used to illustrate the performance of the proposed methodology. 

最新讲座通知（2）

我院于今年年初聘请的客座教授（学校聘） Prof. N.Balakrishnan，将与7月13日（星期一）来我院，在13日15：30pm为他举行一个简短的受聘仪式，仪式后请他为我们做学术讲座。 

Title：

Nonparametric Tests for Panel Count Data and Current

Speaker：

Prof. N. Balakrishnan,Department of Mathematics and Statistics,McMaster University,Hamilton, Ontario, Canada L8S 4K1

e-mail：

bala@mcmaster.ca

Abstract：

In the areas of survival analysis and reliability, one often encounters situations where the data collected are either panel counts or on current status. In this talk, I will discuss some new nonparametric test statistics for testing the homogeneity of k groups based on data of these two forms, and establish their asymptotic normality. After presenting some simulational results, I will then take some real-life data from cancer studies and illustrate the usefulness of the proposed test procedures and highlight their advantages compared to the existing methods.

最新讲座通知（3）

我们荣幸地邀请到密执根大学统计系教授朱冀教授于星期五（7月17日）上午10:00在明德主楼1016与我院老师与学生进行学术交流，我们诚恳地邀请各位老师和学生积极参加！

Title：

Partial Correlation Estimation by Joint Sparse Regression Models

Abstract：

In this talk, we propose a computationally efficient approach for selecting non-zero partial correlations under the high-dimension-low-sample-size setting. This method assumes the overall sparsity of the partial correlation matrix and employs sparse regression techniques for model fitting. We illustrate the performance of our method by extensive simulation studies. It is shown that our method performs well in both non-zero partial correlation selection and the identification of hub variables, and also outperforms two existing methods. We then apply our method to a microarray breast cancer data set and identify a set of "hub genes" which may provide important insights on genetic regulatory networks. Finally, we prove that, under a set of suitable assumptions, the proposed procedure is asymptotically consistent in terms of model selection and parameter estimation. This is joint work with Jie Peng, Pei Wang and Nengfeng Zhou. 

历史讲座记录

2008-2009学年第二学期学术讲座历史：

 2008-2009年第一学期学术讲座时间表：

相关英文网页请点击：http://stat.ruc.edu.cn/en/58138.html