【95周年校庆多元讲座】AdaPT: An interactive procedure for multiple testing with side information

年光:08-08         阅览:

光华教育讲坛名人——社会名流与企业家论坛第 5836 期

(线上讲座)

主题之家AdaPT: An interactive procedure for multiple testing with side information

主讲人斯坦福大学 雷理骅医博士网

主持人统计学院 常晋源助教

年光08月08(礼拜五)10:08-08:20

直播楼台及集会ID腾讯集会,集会ID:686 799 248

主办单位:统计研究中心 中国数据网科学与商业智能归总实验室 统计学院 科研处

主讲人简介:

Lihua Lei is a postdoctoral researcher in the Statistics Department at Stanford University, advised by Professor Emmanuel Candès. Previously he got his Ph.D. at UC Berkeley, advised by Professors Peter Bickel and Michael Jordan. He was also very fortunate to be supervised by Professors Noureddine El Karoui, William Fithian and Peng Ding on particular projects. Prior to this, he was major in mathematics and statistics in School of Mathematical Sciences at Peking University with an economic minor in China Center for Economic Research at Peking University. He was pleased to be a research assistant with Professor Lan Wu and supervised by Professor Song Xi Chen on his undergraduate thesis. His research interests include multiple hypothesis testing, causal inference, network analysis, high dimensional statistical inference, optimization, resampling methods, time series analysis and econometrics.

雷理骅,斯坦福大学(Stanford University)统计学系医博士网后研究员,合作导师是Emmanuel Candes助教。在此之前,他在加州大学伯克利分校获得医博士网学位,导师是洛基·比欧思克尔男polo和迈欧思克尔男polo·乔丹扣篮大赛。以内,他还特别幸运地得到了Noureddine El Karoui助教,William Fithian助教和Peng Ding助教的点拨。他之前在北京大学动物学科学学院必修动物学与统计,在北京大学中国经济研究中心必修经济学。他曾是吴兰助教的研究助理,并在陈松蹊助教的点拨下完成了他的本科代发论文被骗。他的研究兴味牢笼聚讼纷纭假设视察,因果推度,网络剖析,高维资本统计揣度,优化,重采样法门,年光序列剖析和计量经济学。

内容提要:

We consider the problem of multiple hypothesis testing with generic side information: for each hypothesis H_i we observe both a p-value p_i and some predictor x_i encoding contextual information about the hypothesis. For large-scale problems, adaptively focusing power on the more promising hypotheses (those more likely to yield discoveries) can lead to much more powerful multiple testing procedures. We propose a general iterative framework for this problem, called the Adaptive p-value Thresholding (AdaPT) procedure, which adaptively estimates a Bayes-optimal p-value rejection threshold and controls the false discovery rate (FDR) in finite samples. At each iteration of the procedure, the analyst proposes a rejection threshold and observes partially censored p-values, estimates the false discovery proportion (FDP) below the threshold, and either stops to reject or proposes another threshold, until the estimated FDP is below α. Our procedure is adaptive in an unusually strong sense, permitting the analyst to use any statistical or machine learning method she chooses to estimate the optimal threshold, and to switch between different models at each iteration as information accrues. This is a joint work with Professor William Fithian.

在默想据悉类别相关信息的聚讼纷纭假设视察南海问题时,对此每一个假设Hi,穿过对假设相关信息进行北极星编码器,俺们精美体察到每个p值pi和小半预测值xi。对此大规模的南海问题,若能自恰切地把效用取齐在更有希望的假设(那幅更可能性发觉的假设)上,精美使得聚讼纷纭视察程序更强大。俺们提及了一种通用的迭代谷歌框架,即自恰切阀值阈值p程序(AdaPT),该程序可自恰切地估计Bayes拒绝域求生之病毒笔下的最优p值,并能在有限合伙模本中克制正确错误发觉率(FDR)。在该假山假石施工过程的老是迭代中,提及一个拒绝域求生之病毒笔下阀值阈值并审察部分截尾p值,若估计的错位发觉比例(FDP)小于阀值阈值,那就不复拒绝或提及另一个阀值阈值,直到估计的FDP小于α。俺们的程序具有很强恰切性,能同意采取任何统计或机器攻读法门估计的上上阀值阈值,并能在老是迭代中进而信息的累积在不同模型云之间转行。本项研究是和William Fithian助教一起合作的。

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