September 11, 2014 [Department of Mathematics and Statistics, Binghamton University]

This page is read only. You can view the source, but not change it. Ask your administrator if you think this is wrong.

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

~~META:title =September 11, 2014~~
<WRAP 70% center>
|  **DATE:**|Thursday, September 11, 2014 |
|  **TIME:**|1:15pm to 2:15pm |
|  **PLACE:**|OW 100E |
|  **SPEAKER:**|Guang Cheng (Purdue University) |
|  **TITLE:**|Semi-Nonparametric Inference for Massive Data |
</WRAP>
\\ 

<WRAP center box 80%>
<WRAP centeralign>**Abstract**</WRAP>
In this talk, we consider semi-nonparametric inference for massive data that are either homogeneous or heterogeneous. In particular, we propose an aggregation type estimator that follows the "oracle rule" that it shares the same (non-asymptotic) minimax optimal rate and asymptotic distribution as the oracle estimator that is computed as if there were no heterogeneity. Some related problems such as efficiency boosting for heterogeneity estimation and heterogeneity testing will also be discussed. One technical by-product is the statistical inferences for the general kernel ridge regression.
</WRAP>
<WRAP center 40%>
^  Itinerary  ||
|  12 - 1| Lunch |
|  1:15 - 2:15| Talk |
|  2:30 - 3| Qiqing Yu |
|  3 - 3:30| Anton Schick |
|  3:30 - 6| Ganggang Xu |
|  6 - 8| Dinner (Xingye Qiao) |
</WRAP>