報告人: 蔣學軍副教授 南方科技大學
時間：2023年11月17日 10:30-12:00
地點：數(shù)學與信息學院201報告廳
Abstract: Strong correlation among predictors poses a great challenge in the analysis of ultra-high dimensional data. This leads to an increase in the computation time for screening active variables and a decrease in selection accuracy. To address this issue, we propose an innovative two-stage feature selection procedure and its derivative versions based on sparsity assumption. This approach initially screens important features by ranking quantile ridge estimation and subsequently employs a likelihood-based post-screening selection strategy to refine variable selection. This procedure is simple to implement and maintains the robustness of quantile regression. In addition, it possesses other numerous other desirable properties from theoretical and computational standpoints. Theoretically, we establish the strong consistency of feature selection for the proposed methods under some regularity conditions. In empirical studies, we assess the finite sample performance of our methods by comparing them with marginal quantile utility screening approaches and existing penalized quantile regression methods. Furthermore, we apply our methods to identify genes associated with anticancer drug sensitivities.
報告人簡介: 蔣學軍,，南方科技大學統(tǒng)計與數(shù)據(jù)科學系副教授（長聘），博士生導師，于2009年博士畢業(yè)于香港中文大學統(tǒng)計系,，2009-2010在港中文從事博士后研究,，2010-2013任中南財經(jīng)政法大學副教授，于2013年07月加入南方科技大學,，入選深圳市海外高層次人才孔雀計劃（2016）,，曾獲南方科技大學杰出教學獎（2018），深圳市優(yōu)秀教師（2018）,，主持和完成國家（廣東?。┳匀豢茖W基金、深圳市基礎研究面上項目等10余項,。其主要研究方向包括分位數(shù)回歸,、變量選擇、假設檢驗,、高維統(tǒng)計推斷,，金融統(tǒng)計與計量等，已發(fā)表包括Bernoulli Journal , Statistica Sinica, Econometrics Journal, Science China-Mathematics等在內的SCI&SSCI論文50余篇,，授權專利1項,，并出版英文教材一部。國內學會任職主要有中國現(xiàn)場統(tǒng)計研究會-教育統(tǒng)計與管理分會副理事長,，多元分析應用專業(yè)委員會秘書長等,。

歡迎廣大師生參加！