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--- seminars:stat:170831 [2017/09/01 18:18] – qiao
+++ seminars:stat:170831 [2017/09/01 18:21] (current) – qiao
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+<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
+~~META:title =August 31, 2017~~
+<WRAP 70% center>
+^  **DATE:**|Thursday, August 31, 2017 |
+^  **TIME:**|1.15p-2.15p |
+^  **LOCATION:**|WH 100E |
+^  **SPEAKER:**|Qiqing Yu, Binghamton University |
+^  **TITLE:**|The Marginal Distribution Approach For Testing Independence And Goodness-of-fit In Linear Models  |
+</WRAP>
+\\
+<WRAP center box 80%>
+<WRAP centeralign>**Abstract**</WRAP>
+We propose a test to simultaneously test the assumption of independence and goodness-of-fit for a linear regression model $Y=\beta X+W$, where $\beta\in R^p$. If $E(|Y||X)=\infty$, then all existing tests are not valid and their levels
+with a nominal size $0.05$ can be as large as $0.9$. Our approach is valid even if $E(|Y||X)=\infty$ or $E(||X||)=\infty$. Thus it is more realistic than all the existing tests. Our approach is based on the difference between two estimators of the marginal distribution $F_Y$, and thus it is called the MD approach.
+We establish the consistency of the MD test. We compare the MD approach to the existing tests such as
+the test in R package``gam" or the test in Sen and Sen (2014)  through simulation studies.
+If the existing tests are valid, then none of the existing tests and the MD test is uniformly more powerful than the other. We apply the MD  approach to 3 real data sets.
+</WRAP>