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--- seminars:stat:170202 [2017/01/31 19:58] – created sdang
+++ seminars:stat:170202 [2017/01/31 20:01] (current) – sdang
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+~~META:title =February 26, 2017~~
+<WRAP 70% center>
+^  **DATE:**|Thursday, February 2, 2017 |
+^  **TIME:**|1.15p-2.15p |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Anton Schick, Binghamton University |
+^  **TITLE:**|Weighted least squares estimation with missing responses: An empirical likelihood approach |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+A heteroscedastic linear regression model is considered where responses
+are allowed to be missing at random. An estimator s constructed that matches the
+performance of the weighted least squares estimator without the knowledge of the
+conditional variance function. This is usually done by constructing an estimator of the
+variance function. Our estimator is a maximum empirical likelihood estimator based on
+an increasing number of estimated constraints and avoids estimating the variance function.
+</WRAP>