Local Polynomial-Brunk Estimation in Semi-Parametric Monotone Errors-in-Variables Model with Right-Censored Data  

作　　者：XIA Wei CHEN Zhao WU Wuqing ZHOU Jianjun 

机构地区：[1]Department of Finance and Statistics, University of Science and Technology,Hefei [2]Department of Operations Research and Financial Engineering, Princeton University [3]School of Business, Renmin University of China [4]School of Mathematics and Statistics, Yunnan University

出　　处：《Journal of Systems Science & Complexity》2015年第4期938-960,共23页系统科学与复杂性学报（英文版）

基　　金：supported by Fundamental Research Funds for the Central Universities;the Research Funds of Renmin University of China under Grant No.11XNK027

摘　　要：This paper introduces a semi-parametric model with right-censored data and a monotone constraint on the nonparametrie part. The authors study the local linear estimators of the parametric coefficients and apply B-spline method to approximate the nonparametric part based on grouped data. The authors obtain the rates of convergence for parametric and nonparametric estimators. Moreover, the authors also prove that the nonparametric estimator is consistent at the boundary. At last, the authors investigate the finite sample performance of the estimation.

关 键 词：B-SPLINE grouped brunk local polynomial monotone regression right-censored semi-parametric model. 

分 类 号：O212.7[理学—概率论与数理统计] O212.1[理学—数学]