Estimation of Multiple Linear Regression Model with Twice-Censored Data

@article{Shen2015EstimationOM,
  title={Estimation of Multiple Linear Regression Model with Twice-Censored Data},
  author={Pao-sheng Shen},
  journal={Communications in Statistics - Theory and Methods},
  year={2015},
  volume={44},
  pages={4631 - 4640},
  url={https://api.semanticscholar.org/CorpusID:125366977}
}
  • P. Shen
  • Published 2 November 2015
  • Mathematics
  • Communications in Statistics - Theory and Methods
In this article, we propose three M-estimators for multiple regression model when response variable is subject to twice censoring. The consistency of the proposed M-estimators is established. A simulation study is conducted to investigate the performance of the proposed estimators. Furthermore, the simple bootstrap methods are used to construct interval estimators. 
1 Citation

Relative Error Prediction for Twice Censored Data

In this paper we consider the problem of non-parametric relative regression for twice censored data. We introduce and study a new estimate of the regression function when it is appropriate to assess

Regression Analysis with Randomly Right-Censored Data

This paper proposes a new estimator of the parameter vector in a linear regression model when the observations are randomly censored on the right and when the error distribution is unknown. This

Estimation in a Linear Regression Model with Censored Data

We consider the semiparametric linear regression model with censored data and with unknown error distribution. We describe estimation equations of the Buckley-James type that admit √n-consistent and

Linear regression with censored data

SUMMARY We give a method of estimating parameters in the linear regression model which allows the dependent variable to be censored and the residual distribution to be unspecified. The method differs

Regression M-estimators with doubly censored data

The M-estimators are proposed for the linear regression model with random design when the response observations are doubly censored. The proposed estimators are constructed as some functional of a

Nonparametric estimators of the survival function with twice censored data

Patilea and Rolin (Ann Stat 34(2):925–938, 2006) proposed a product-limit estimator of the survival function for twice censored data. In this article, based on a modified self-consistent (MSC)

Nonparametric Estimation of a Survivorship Function with Doubly Censored Data

Abstract A simple iterative procedure is proposed for obtaining estimates of a response time distribution when some of the data are censored on the left and some on the right. The procedure is based

A Missing Information Principle and $M$-Estimators in Regression Analysis with Censored and Truncated Data

A general missing information principle is proposed for constructing Mestimators of regression parameters in the presence of left truncation and right censoring on the observed responses. By making

Product-limit estimators of the survival function with twice censored data

A model for competing (resp. complementary) risks survival data where the failure time can be left (resp. right) censored is proposed. Product-limit estimators for the survival functions of the