An Improved Interior Point Algorithm for Quantile Regression
作者： Pan Zhao , Shenghua Yu
DOI: 10.1109/ACCESS.2020.3012871
关键词:
摘要: Quantile regression is a powerful statistical technique for estimating the quantiles of conditional distribution on values covariates. It has been widely used in many fields. In this paper, an improved interior point algorithm quantile proposed. The introduces multiple centrality corrections into regression. purpose introducing to reduce overall solution time required solve problem. computational experiments results constitute evidence improvement obtained with use correction combined algorithm.
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