| 主讲人 |
邹长亮 教授 |
简介 |
<p><span style="font-family: arial, sans-serif; font-size: 13px">Abstract: Monitoring high-dimensional data streams has become</span></p>
<div style="font-family: arial, sans-serif; font-size: 13px">increasingly important for real-time detection of abnormal</div>
<div style="font-family: arial, sans-serif; font-size: 13px">activities in many data-rich applications. In this talk, I will give</div>
<div style="font-family: arial, sans-serif; font-size: 13px">a brief discussion on recent works from the following aspects: i)</div>
<div style="font-family: arial, sans-serif; font-size: 13px">develop an efficient global monitoring procedure when we do not know</div>
<div style="font-family: arial, sans-serif; font-size: 13px">which subset of data streams is affected by an occurring event; ii)</div>
<div style="font-family: arial, sans-serif; font-size: 13px">suggest a procedure which is able to control the conditional false</div>
<div style="font-family: arial, sans-serif; font-size: 13px">discovery rate at each time point when our focus is detecting</div>
<div style="font-family: arial, sans-serif; font-size: 13px">changes in each individual data stream; iii) propose a</div>
<div style="font-family: arial, sans-serif; font-size: 13px">distribution-free detection scheme in the sense that its in-control</div>
<div style="font-family: arial, sans-serif; font-size: 13px">run-length distribution is free of the underlying distribution.</div>
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