
<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>

~~META:title =December 11, 2014~~
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|  **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  |
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<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.
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