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--- seminars:stat:141211 [2014/11/20 20:32]
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+~~META:title =December 11, 2014~~
+<WRAP 70% center>
+|  **DATE:**|Thursday, December 11, 2014 |
+|  **TIME:**|1:15pm to 2:15pm |
+|  **PLACE:**|OW 100E |
+|  **SPEAKER:**|Yilin Zhu (Binghamton University) |
+|  **TITLE:**|Efficient Estimation In Various Regression Model With Possibly Missing Responses  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+We considered parametric estimation and error estimation in
+two classical regression models. First, a heteroscedastic linear
+regression model is considered where responses are allowed to be
+missing at random and with conditional variance modeled as a function
+of the mean response. Maximum empirical likelihood estimation is
+studied for an empirical likelihood with an increasing number of
+estimated constraints. The resulting estimator is shown to be
+asymptotically normal and can perform outperform the ordinary least
+squares estimator. Second, we proved a stochastic expansion for a
+residual-based estimator of the error distribution in semi-parametric
+model. It implies a functional central limit theorem.
+</WRAP>