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seminars:stat:10152015 [2015/08/31 15:41] – qiaoseminars:stat:10152015 [2015/08/31 15:41] (current) – qiao
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 +<WRAP centeralign>##Statistics Seminar##\\ Department of Mathematical Sciences</WRAP>
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 +~~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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