Document Type
Lecture
Publication Date
10-13-2011
Abstract
We propose a self-consistency based method to conduct local linear estimation of mean functions for general regression models with censored data. Self-consistent estimation techniques are frequently used to obtain consistent estimation of survival functions when data are censored. Such techniques have also been proven useful for estimation of linear regression parameters in censored setting. We extend such methods to local linear estimation of the regression functions for nonparametric models. The bandwidth will be selected adaptively according to the data. Simulation studies are conducted. Some basic asymptotic results are also obtained. *This work was done jointly with Dr. K. Kulasekera, Clemson University
Relational Format
presentation
Recommended Citation
Zhao, Meng, "Local Linear Regression with Censored Data" (2011). Probability & Statistics Seminar. 43.
https://egrove.olemiss.edu/math_statistics/43
