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"><span style='font-size:22.0pt;font-family:"Arial",sans-serif;color:#286DC0;text-decoration:none'><img border=0 width=150 height=49 style='width:1.5625in;height:.5104in' id=logo src="cid:image001.jpg@01D94508.C2472E40" alt="Department of Statistics and Data Science
"></span></a></span><span style='font-family:"Arial",sans-serif;color:black'> <a href="https://statistics.yale.edu/" title=Home><b><span style='font-size:22.0pt;color:#286DC0'>Department of Statistics and Data Science </span></b></a></span><b><i><u><span style='font-size:22.0pt;font-family:"Arial",sans-serif;color:#286DC0'> <o:p></o:p></span></u></i></b></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif'>In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation (<a href="https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa"><span style='color:windowtext;text-decoration:none'>https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa</span></a>)<o:p></o:p></span></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif'><a href="https://0.0.0.10/"><span style='color:windowtext;text-decoration:none'>3:30pm</span></a> - Pre-talk meet and greet teatime - Dana House, 24 Hillhouse Avenue </span><o:p></o:p></p><h1 style='mso-margin-top-alt:.1in;margin-right:0in;margin-bottom:0in;margin-left:0in'><span style='font-size:12.0pt;font-family:"Arial",sans-serif;text-transform:uppercase'>ZHIMEI REN</span><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>, </span><span class=odd><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>University of Chicago</span></span><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p></o:p></span></h1><p class=MsoNormal><!--[if gte vml 1]><v:shapetype id="_x0000_t75" coordsize="21600,21600" o:spt="75" o:preferrelative="t" path="m@4@5l@4@11@9@11@9@5xe" filled="f" stroked="f">
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</v:shape><![endif]--><![if !vml]><img width=123 height=148 style='width:1.2812in;height:1.5416in' src="cid:image003.jpg@01D94508.C2472E40" align=left hspace=12 v:shapes="Picture_x0020_1"><![endif]><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p></o:p></span></p><p class=MsoNormal><span class=date-display-single><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Date: Monday, February 20, 2023<o:p></o:p></span></span></p><p class=MsoNormal><span class=date-display-single><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Time: </span></span><span class=date-display-start><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>4:00PM</span></span><span class=date-display-range><span style='font-size:12.0pt;font-family:"Arial",sans-serif'> to </span></span><span class=date-display-end><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>5:00PM</span></span><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p></o:p></span></p><p class=MsoNormal><span class=fn><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Location: Mason Lab 211</span></span><span style='font-size:12.0pt;font-family:"Arial",sans-serif'> <span class=map-icon><span style='letter-spacing:.6pt'><a href="http://maps.google.com/?q=9+Hillhouse+Ave%2C+New+Haven%2C+CT%2C+06511%2C+us"><span style='color:windowtext'>see map</span></a> </span></span><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>9 Hillhouse Ave<o:p></o:p></span></p><p class=MsoNormal><span class=locality><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>New Haven</span></span><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>, <span class=region>CT</span> <span class=postal-code>06511</span><o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><a href="https://zhimeir.github.io/"><span style='color:windowtext'>Website</span></a><b> <o:p></o:p></b></span></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></b></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Title: Stable Variable Selection with Knockoffs<o:p></o:p></span></b></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p class=MsoNormal><b><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>Information and Abstract: <o:p></o:p></span></b></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;box-sizing: inherit'><span style='font-size:12.0pt;font-family:"Arial",sans-serif'>A common problem in many modern statistical applications is to find a set of important variables—from a pool of many candidates—that explain the response of interest. For this task, model-X knockoffs offers a general framework that can leverage any feature importance measure to produce</span> a variable selection algorithm: it discovers true effects while rigorously controlling the number or fraction of false positives, paving the way for reproducible scientific discoveries. The model-X knockoffs, however, is a randomized procedure that relies on the one-time construction of synthetic (random) variables. Different runs of model-X knockoffs on the same dataset often result in different sets of selected variables, which is not desirable for the reproducibility of the reported results.<o:p></o:p></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;box-sizing: inherit'>In this talk, I will introduce derandomization schemes that aggregate the selection results across multiple runs of the knockoffs algorithm to yield stable selection. In the first part, I will present a derandomization scheme that controls the number of false positives, i.e., the per family error rate (PFER) and the k family-wise error rate (k-FWER). In the second part, I will talk about an alternative derandomization scheme with provable false discovery rate (FDR) control. Equipped with these derandomization steps, the knockoffs framework provides a powerful tool for making reproducible scientific discoveries. The proposed methods are evaluated on both simulated and real data, demonstrating comparable power and dramatically lower selection variability when compared with the original model-X knockoffs.<o:p></o:p></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;box-sizing: inherit'><strong><i><span style='font-family:"Calibri",sans-serif'>In-Person seminars will be held at Mason Lab 211, 9 Hillhouse Avenue with the option of virtual participation (</span></i></strong><a href="https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa"><span style='color:#286DC0'>https://yale.hosted.panopto.com/Panopto/Pages/Sessions/List.aspx?folderID=f8b73c34-a27b-42a7-a073-af2d00f90ffa</span></a>)<o:p></o:p></p><p style='mso-margin-top-alt:0in;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;box-sizing: inherit'><strong><i><span style='font-family:"Calibri",sans-serif'><a href="https://0.0.0.10/"><span style='color:#286DC0'>3:30pm</span></a> - Pre-talk meet and greet teatime - Dana House, 24 Hillhouse Avenue </span></i></strong><o:p></o:p></p><p class=MsoNormal><b><i><o:p> </o:p></i></b></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Arial",sans-serif;color:black'>For more details and upcoming events visit our website at <a href="http://statistics.yale.edu/"><span style='color:black'>http://statistics.yale.edu/</span></a></span><o:p></o:p></p><p class=MsoNormal><span style='font-family:"Arial",sans-serif'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:18.0pt;font-family:"Arial",sans-serif'>Department of Statistics and Data Science<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial",sans-serif;color:black'>Yale University<br>24 Hillhouse Avenue<br>New Haven, CT 06511<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial",sans-serif;color:black'>t 203.432.0666<br>f 203.432.0633<o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>