Speaker: Xi Chen

Abstract: In this talk, we discuss the inference problem of quantile regression for a large sample size n but under a limited memory constraint, where the memory can only store a small batch of data of size m. A popular approach, the naive divide-and-conquer method, only works when n=o(m^2) and is computationally expensive. This talk proposes a novel inference approach and establishes the asymptotic normality result that achieves the same efficiency as the quantile regression estimator computed on all the data. Essentially, our method can allow arbitrarily large sample size n as compared to the memory size m. Our method can also be applied to address the quantile regression under distributed computing environment (e.g., in a large-scale sensor network) or for real-time streaming data. This is a joint work with Weidong Liu and Yichen Zhang.