講座編號(hào)：jz-yjsb-2022-y017
講座題目：Activation discovery with FDR control Application to fMRI data
主 講 人：王兆軍 教授 南開(kāi)大學(xué)
講座時(shí)間：2022年5月11日（星期三）下午14:00
講座地點(diǎn)：騰訊會(huì)議，會(huì)議ID:518 222 217
參加對(duì)象：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院全體教師及研究生
主辦單位：數(shù)學(xué)與統(tǒng)計(jì)學(xué)院、研究生院
主講人簡(jiǎn)介：
王兆軍，南開(kāi)大學(xué)統(tǒng)計(jì)與數(shù)據(jù)科學(xué)學(xué)院教授、博士生導(dǎo)師、執(zhí)行院長(zhǎng)和黨總支書(shū)記，統(tǒng)計(jì)研究院院長(zhǎng)，中國(guó)現(xiàn)場(chǎng)統(tǒng)計(jì)研究會(huì)副理事長(zhǎng)，中國(guó)工業(yè)統(tǒng)計(jì)教學(xué)研究會(huì)副會(huì)長(zhǎng)，天津數(shù)據(jù)科學(xué)與技術(shù)學(xué)會(huì)理事長(zhǎng)，天津市學(xué)位委員會(huì)數(shù)學(xué)與統(tǒng)計(jì)學(xué)科評(píng)議組召集人，曾獲國(guó)務(wù)院政府特殊津貼，全國(guó)百篇優(yōu)博指導(dǎo)教師，教育部全國(guó)高校自然科學(xué)獎(jiǎng)二等獎(jiǎng)及天津市自然科學(xué)獎(jiǎng)一等獎(jiǎng)。王兆軍教授的主要研究方向包括統(tǒng)計(jì)過(guò)程控制(SPC)、非(半)參數(shù)回歸、降維、高維數(shù)據(jù)分析、變點(diǎn)等，已在Journal of the American Statistical Association、Annals of Statistics、Biometrika、Statistica Sinica等專(zhuān)業(yè)頂級(jí)期刊上發(fā)表高質(zhì)量學(xué)術(shù)論文110余篇，先后主持國(guó)家自然科學(xué)基金重點(diǎn)項(xiàng)目、面上項(xiàng)目、教育部博士點(diǎn)基金項(xiàng)目等10余項(xiàng)，現(xiàn)擔(dān)任Statistical Theory and Related Fields、《統(tǒng)計(jì)信息論壇》、《數(shù)學(xué)進(jìn)展》等雜志編委和《數(shù)理統(tǒng)計(jì)與管理》等雜志副主編。
主講內(nèi)容：
Data arriving in “streams” from a large number of sources is ubiquitous, a portion of which usually incurs structural changes during the time-course of data acquisition. For example, in fMRI analysis, some brain regions become active associated with task-related stimuli or even in resting-states. Such a region corresponds to an activated data stream. We are aiming to measure the uncertainty of discovering data streams in activation via the tool of the false discovery rate (FDR). Borrowing ideas from recent developments of the FDR control methodologies, we propose a simple yet effective method to achieve this purpose meanwhile taking unknown asynchronous change patterns and spatial dependence into consideration. Its validity on controlling the FDR is justified by asymptotic analysis. Numerical experiments indicate that the proposed method is both accurate and powerful. It is also applied in a real fMRI data analysis. A R package SLIP is developed to implement the proposed method.