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Incorporating graphical structure of predictors in sparse quantile regression
作者:      发布时间:2020-10-24      
报告时间 2020年10月28日13:00 报告地点 腾讯会议(会议ID:886 452 911)
报告人 刘显慧(江西财经大学)

报告名称:Incorporating graphical structure of predictors in sparse quantile regression

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报告专家:刘显慧

专家所在单位:江西财经大学统计学院

报告时间:2020年10月28日13:00-14:00

报告地点:腾讯会议(ID:886 452 911)

专家简介:刘显慧,博士毕业于中国科学技术大学,研究方向为生存分析、变量选择。在中国科学:数学、Computational Statistics and Data Analysis、Journal of Statistical Planning and Inference等国内外著名SCI期刊发表论文数篇。

报告摘要:Quantile regression in high dimensional settings is useful in analyzing high dimensional heterogeneous data. In this paper, different from existing methods in quantile regression which treat all the predictors equally with the same priori, we take advantage of the graphical structure among predictors to improve the performance of parameter estimation, model selection and prediction in sparse quantile regression. It is shown under mild conditions that the proposed method enjoys the model selection consistency and the oracle properties. An alternating direction method of multipliers (ADMM) algorithm with a linearization technique is proposed to implement the proposed method numerically, and its convergence is justified. Simulation studies are conducted, showing that the proposed method is superior to existing methods in terms of estimation accuracy and predictive power. The proposed method is also applied to a real dataset.

邀请人:刘展


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