Distribution Regression
报 告 人:: 周望
报告地点:: 数学与统计学院四楼报告厅
报告时间:: 2017年12月28日星期四15:00-16:00

 Linear regression is a fundamental and popular statistical method. There are various kinds of linear regression, such as mean regression and quantile regression. In this paper, we propose a new one called distribution regression, which allows broad-spectrum of the error distribution in the linear regression. Our method uses nonparametric technique to estimate regression parameters. Our studies indicate that our method provides a better alternative than mean regression and quantile regression under many settings, particularly for asymmetrical heavy-tailed distribution or multimodal distribution of the error term.

Under some regular conditions, our estimator is -consistent and possesses the asymptotically normal distribution. The proof of the asymptotic normality of our estimator is very challenging because our nonparametric likelihood function cannot be transformed into sum of independent and identically distributed random variables. Furthermore, penalized likelihood estimator is proposed and enjoys the so-called oracle property with diverging number of parameters. Numerical studies also demonstrate the effectiveness and the flexibility of the proposed method.


发 布 人:科研助理 发布时间: 2017-12-26
周望, 2004年7月起在新加坡国立大学统计系任教,并于2009年1月获终身教授。现为新加坡国立大学正教授。 主要研究方向为: random matrices, SLE, high dimensional statistics。近年来发表有较高学术水平的论文五十多篇。 其中在概率统计学方面的国际公认的顶尖杂志Annals of Statistics, Journal of American Statistical Association, Biometrika, Annals of Probability, Probability Theory and Related Fields, Annals of Applied Probability上发表论文十余篇。2012获得国际统计学会当选成员(Elected Member of International Statistical Institute)。 2012年获得新加坡国立大学 “杰出科学家奖”。2005年起主持新加坡政府基金项目十余项。