##Statistics Seminar##\\ Department of Mathematical Sciences ~~META:title =October 15, 2015~~ ^ **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 | \\ **Abstract** 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.