发件人: 北京大学统计科学中心 <
stat-c...@pku.edu.cn>
日期: 2012年12月27日 GMT+0800下午12时15分20秒
收件人: undisclosed-recipients:;<>
主题: seminar notice(2013.01.10,Prof.Bin Yu,University of California at
Berkeley)
北京大学统计科学中心
Title（题目）：Stability
Speaker（报告人）：Prof.Bin Yu,
Departments of Statistics and EECS,
University of California at Berkeley
www.stat.berkeley.edu/~binyu Time（时间）：2013-01-10(星期四) 10:30-11:30
Place（地点）：数学学院理科1号楼1114室
Abstract（摘要）：Reproducibility is imperative for any scientific
discovery. More often than not, modern scientific findings rely on
statistical analysis of high-dimensional data. At a minimum,
reproducibility manifests itself in stability of statistical results
relative to “reasonable” perturbations to data and to the model used.
Jacknife, bootstrap, and cross-validation are based on perturbations to
data, while robust statistics methods deal with perturbations to models.
In this article, a case is made for the importance of stability in
statistics. Firstly, we motivate the necessity of stability of
interpretable encoding models for movie
reconstruction from brain fMRI
signals. Secondly, we find strong evidence in the literature to
demonstrate the central role of stability in statis- tical inference.
Thirdly, a smoothing parameter selector based on estimation stability
(ES), ES-CV, is proposed for Lasso, in order to bring stability to bear
on cross-validation (CV). ES-CV is then utilized in the encoding models
to reduce the number of predictors by 60% with almost no loss (1.3%) of
prediction performane across over 2,000 voxels. Last, a novel “stability”
argument is seen to drive new results that shed light on the intriquing
interactions between sample to sample varibility and heavier tail error
distribution (e.g. double-exponential) in high
dimensional regression
models with p predictors and n independent samples. In particular, when
p/n → κ ∈ (0.3, 1) and error is double-exponential, OLS is a better
estimator than LAD.
About the speaker（报告人介绍）：Bin Yu is Chancellor's Professor in the
Departments of Statistics and of Electrical Engineering & Computer
Science at UC Berkeley.She has published over 100 scientific papers in
premier journals in Statistics, EECS, remote sensing and neuroscience, in
a wide range of research areas including empirical process theory,
information theory(MDL), MCMC methods, signal processing, machine
learning, high dimensional data inference (boosting and Lasso and sparse
modeling
in general), and interdisciplinary data problems. She has served
on many editorial boards for journals such as Annals of Statistics,
Journal of American Statistical Association,and Journal of Machine
Learning Research. She was a 2006 Guggenheim Fellow, co-recipient of the
Best Paper Award of IEEE Signal Processing Society in 2006, and the 2012
Tukey Memorial Lecturer of the Bernoulli Society (selected every four
years).She is a Fellow of AAAS, IEEE, IMS (Institute of Mathematical
Statistics) and ASA (American Statistical Association). She is currently
President-Elect of IMS (Institute of Mathematical Statistics). She is
serving on the Scientific Advisory Board of IPAM (Institute of Pure and
Applied Mathematics) and on
the Board of Mathematical Sciences and
Applications of NAS. She was co-chair of the National Scientific
Committee of SAMSI (Statistical and Applied Mathematical Sciences
Institute), and on the Board of Governors of IEEE-IT Society.
我们期待您的参加。
祝好！
北大统计科学中心
2012-12-27
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Peking university, Beijing China
Telephone：010-62760736
Email:
stat-c...@pku.edu.cn http://www.stat-center.pku.edu.cn/
