期刊名称：Sankhya. Series A, mathematical statistics and probability
印刷版ISSN：0976-836X
电子版ISSN：0976-8378
出版年度：2005
卷号：67
期号：02
出版社：Indian Statistical Institute
摘要：In this paper, we discuss some practical computational issues for quantile regression. We consider the computation from two aspects: estimation and inference. For estimation, we cover three algorithms: simplex, interior point, and smoothing. We describe and compare these algorithms, then discuss implementation of some computing techniques, which include optimization, parallelization, and sparse computation, with these algorithms in practice. For inference, we focus on confidence intervals. We discuss three methods: sparsity, rank-score, and resampling. Their performances are compared for data sets with a large number of covariates.
关键词：Quantile regression, host optimization, multithreading, sparse computing, smoothing algorithm, simplex, interior point, median regression, linear programming, preprocessing.
