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+<WRAP centeralign>##Data Science Seminar##\\ Hosted by the Department of Mathematics and Statistics</WRAP>
+  * Date: Tuesday, October 3, 2023
+  * Time: 12:00pm -- 1:00pm
+  * Room: Whitney Hall 100E
+  * Speaker: Dr. HaiYing Wang  (University of Connecticut)
+  * Title: Rare Events Data and Maximum Sampled Conditional Likelihood.
+<WRAP center box 80%>
+<WRAP centeralign>**//Abstract//**</WRAP>
+ \\
+In this talk, we show that the available information about unknown parameters in
+rare events data is only tied to the relatively small number of cases, which
+justifies the usage of negative sampling. However, if the negative instances are
+subsampled to the same level of the positive cases, there is information
+loss. We derive an optimal sampling probability for the inverse probability
+weighted (IPW) estimator to minimize the information loss. We further propose
+a likelihood-based estimator to further improve the estimation efficiency, and
+show that the improved estimator has the smallest asymptotic variance among a
+large class of estimators. It is also more robust to pilot misspecification. The
+likelihood-based estimator is also generalized to a class of models beyond
+binary response models. We validate our approach on simulated data, the MNIST
+data, and a real click-through rate dataset with more than 0.3 trillion
+instances.
+ \\
+Biography of the speaker: Dr. Wang is an Associate Professor in the Department of Statistics at the
+University of Connecticut. He obtained his Ph.D. from the Department of
+Statistics at the University of Missouri in 2013, and his M.S. from the Academy
+of Mathematics and Systems Science, Chinese Academy of Sciences in 2006. His
+research interests include informative subdata selection for big data, model
+selection, model averaging, measurement error models, and semi-parametric
+regression.
+</WRAP>