October 15, 2015 [Department of Mathematics and Statistics, Binghamton University]

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

~~META:title =October 15, 2015~~
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^  **DATE:**|Thursday, October 15, 2015 |
^  **TIME:**|1:15pm to 2:15pm |
^  **LOCATION:**|WH 100E |
^  **SPEAKER:**|Shih-Kang Chao, Department of Statistics, Purdue University |
^  **TITLE:**|Quantile Regression for Extraordinarily Large Data |
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<WRAP centeralign>**Abstract**</WRAP>
One complexity of massive data comes from the accumulating errors that are often unknown and may even have varying shapes as data grows. In this talk, we consider a general quantile-based modelling that even allows the unknown error distribution to be arbitrarily different across all sub-populations. A delicate analysis on the computational-and-statistical tradeoff is further carried out based on nonparametric sieve estimation.
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