时间：2018年11月27日（周二） 下午15：30-16：30
地点：理科大楼A楼1716（中北校区）
题目：A spline-assisted semiparametric approach to nonparametric measurement error models
报告人：Ma Yanyuan
摘要：
Nonparametric estimation of the probability density function of a random variable measured with error is considered to be a diffcult problem, in the sense that depending on the measurement error property, the estimation rate can be as slow as the logarithm of the sample size. Likewise, nonparametric estimation of the regression function with errors in the covariate suers the same possibly slow rate. The traditional methods for both problems are based on deconvolution, where the slow convergence rate is caused by the quick convergence to zero of the Fourier transform of the measurement error density, which, unfortunately, appears in the denominators during the construction of these methods. Using a completely different approach of spline-assisted semiparametric methods, we are able to construct nonparametric estimators of both density functions and regression mean functions that achieve the same nonparametric convergence rate as in the error free case. Other than requiring the error-prone variable distribution to be compactly supported, our assumptions arenot stronger than in the classical deconvolution literatures. The performance of these methods are demonstrated through some simulations and a data example.
报告人简介：
Yanyuan Ma教授博士毕业于麻省理工学院，现任职于宾夕法尼亚州立大学统计系，Yanyuan Ma教授主要研究领域有降维、测量误差、半参数模型等，已在统计学四大国际顶级期刊Annals of Statistics, JASA, JRSSB, Biometrika上发表30余篇文章。