Abstract </head> <body text="#000000" bgcolor="#FFFFFF" link="#0000EF" vlink="#CF30CF" alink="#FF0000" nosave> <br> <center><h2> <!title>On the probability of correct selection for large k populations, with application to Microarray Data </h2> <br> <h2> <!speaker>Xinping Cui </h2> <h3> (UC Riverside) </h3> </center> <br> One frontier of modern statistical research is the "multiple testing problem" arising from data sets with large k (>1000) populations, e.g. differential expression tests on thousands of genes through microarrays. In this talk, we propose "d-best" and "G-best" estimators on the probability of correctly selecting one or more "best" genes out of k genes under testing and demonstrate how it can be employed as a "meta-method" to discriminate between competing multiple hypothesis testing approaches (eg, maxT, FDR, etc) in identifying differentially expressed genes. We will also explore the use of this new estimator in gene selection in cancer classification. <br><br><br> <a href="index.html">Back to main page</a><br><br> <hr> <p><br><br>  <script type="text/javascript"> var sc_project=2800657; var sc_invisible=0; var sc_partition=28; var sc_security="dd1187b0"; </script> <script type="text/javascript" src="http://www.statcounter.com/counter/counter_xhtml.js"></script><noscript><div class="statcounter"><a class="statcounter" href="http://www.statcounter.com/"><img class="statcounter" src="http://c29.statcounter.com/2800657/0/dd1187b0/0/" alt="free stats" /></a></div></noscript>  </body> </html>