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主讲人 |
蒋滨雁、宋永佳、张琼 |
简介 |
<p> <span style="font-size: 12pt; font-family: 宋体;">报告人：</span><b><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;">Binyan Jiang</span><br />
</b></p>
<p class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">题目：</span><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;">On the sparsity of signals in a random sample</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif""><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">摘要</span><span style="font-size: 12pt; font-family: 宋体;">：</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">This article proposes a method of moments technique for estimating the sparsity of signals in a random sample. This involves estimating the largest eigenvalue of a large Hermitian trigonometric matrix under mild conditions. As illustration, the method is applied to two well-known problems. The first focuses on the sparsity of a large covariance matrix and the second investigates the sparsity of a sequence of signals observed with stationary, weakly dependent noise. Simulation shows that the proposed estimators can have significantly smaller mean absolute errors than their main competitors.<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">Some key words: Large covariance matrix; Method of moments; Signal sequence; Sparsity; Trigonometric matrix. <o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">时间：</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">2014</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">年</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">12</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">月</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">22</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">日</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">14:30-15:30<br />
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<p class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">报告人：</span><b><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;">Yongjia Song <o:p></o:p></span></b></p>
<p class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">题目：</span><b><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;">Risk Averse Stochastic Optimization</span></b><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif""><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">摘要</span><span style="font-size: 12pt; font-family: 宋体;">：</span><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;">In this talk, we will first give an overall introduction to risk averse stochastic optimization, and then discuss some recent progress on chance-constrained stochastic programs. Risk averse stochastic optimization dates back to Markowitz's groundbreaking work on portfolio investment optimization, where risk is addressed in the decision making via a mean-risk objective function. Chance-constrained stochastic program (CCSP) is a convenient risk averse optimization model that controls the probability of bad outcomes. Despite its popularity, CCSP is notoriously challenging to solve because its feasible region is in general non convex. We will focus on integer programming techniques based on various mathematical programming formulations to solve CCSP more efficiently. Numerical examples will be provided to illustrate the effectiveness of these approaches.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">时间：</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">2014</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">年</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">12</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">月</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">22</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">日</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">15:30-16:30<o:p></o:p></span></p>
<p class="MsoNormal"><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif""> </span></p>
<p class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">报告人：</span><b><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;">Qiong Zhang</span></b><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;"><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">题目：</span><b><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;">Statistical Designs for Model Assessment</span></b><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif""><o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size: 12pt; font-family: 宋体;">摘要</span><span style="font-size: 12pt; font-family: 宋体;">：</span><span lang="EN-US" style="font-size: 12pt; font-family: 'Times New Roman', serif;">In this talk, I will present space-filling design approaches to reduce the variability in assessing approximation models for a black-box system. The key of this approach is to generate a structured cross-validation sample such that the input values in each fold achieve uniformity. The advantage of the proposed method<span class="apple-converted-space"> </span>will be demonstrated by theoretical and numerical results.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">时间：</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">2014</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">年</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">12</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">月</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">22</span><span style="font-size:12.0pt;font-family:宋体;mso-ascii-font-family:"Times New Roman";mso-fareast-font-family:宋体;mso-fareast-theme-font:minor-fareast;mso-hansi-font-family:"Times New Roman";mso-bidi-font-family:"Times New Roman"">日</span><span lang="EN-US" style="font-size:12.0pt;font-family:"Times New Roman","serif"">16:30-17:30<o:p></o:p></span></p> |
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主讲人简介 |
<p>蒋滨雁，<span lang="EN-US" style="font-family: 'Times New Roman', serif;">Visiting Research Scientist, Living Analytics Research Centre, Heinz College & Department of Statistics (courtesy appointment) Carnegie Mellon University</span></p>
<p>CV：<a href="/EventsMgr/Upload/File/2014/12/2014121503222988.pdf">EventsMgr/Upload/File/2014/12/2014121503222988.pdf</a></p>
<div><span lang="EN-US" style="font-family: 'Times New Roman', serif;">宋永佳，</span><span style="font-family: 'Times New Roman', serif;">Assistant Professor,</span><span class="apple-converted-space" style="font-family: 'Times New Roman', serif;"> </span><span style="font-family: 'Times New Roman', serif;">Virginia Commonwealth University</span></div>
<div>CV：<a href="/EventsMgr/Upload/File/2014/12/20141215032253424.pdf">EventsMgr/Upload/File/2014/12/20141215032253424.pdf</a></div>
<div> </div>
<div><span style="font-family: 'Times New Roman', serif;">张琼，Visiting Assistant Professor, Commonwealth University</span></div>
<div>CV：<a href="/EventsMgr/Upload/File/2014/12/20141215032406198.pdf">EventsMgr/Upload/File/2014/12/20141215032406198.pdf</a></div> |
期数 |
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