Bias correction on censored least squares regression models

Jesus Orbe; Vicente Núñez-Antón

Kybernetika (2012)

  • Volume: 48, Issue: 5, page 1045-1063
  • ISSN: 0023-5954

Abstract

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This paper proposes a bias reduction of the coefficients' estimator for linear regression models when observations are randomly censored and the error distribution is unknown. The proposed bias correction is applied to the weighted least squares estimator proposed by Stute [28] [W. Stute: Consistent estimation under random censorship when covariables are present. J. Multivariate Anal. 45 (1993), 89-103.], and it is based on model-based bootstrap resampling techniques that also allow us to work with censored data. Our bias-corrected estimator proposal is evaluated and its behavior assessed in simulation studies concluding that both the bias and the mean square error are reduced with the new proposal.

How to cite

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Orbe, Jesus, and Núñez-Antón, Vicente. "Bias correction on censored least squares regression models." Kybernetika 48.5 (2012): 1045-1063. <http://eudml.org/doc/251404>.

@article{Orbe2012,
abstract = {This paper proposes a bias reduction of the coefficients' estimator for linear regression models when observations are randomly censored and the error distribution is unknown. The proposed bias correction is applied to the weighted least squares estimator proposed by Stute [28] [W. Stute: Consistent estimation under random censorship when covariables are present. J. Multivariate Anal. 45 (1993), 89-103.], and it is based on model-based bootstrap resampling techniques that also allow us to work with censored data. Our bias-corrected estimator proposal is evaluated and its behavior assessed in simulation studies concluding that both the bias and the mean square error are reduced with the new proposal.},
author = {Orbe, Jesus, Núñez-Antón, Vicente},
journal = {Kybernetika},
keywords = {bias; censoring; least squares; linear regression; Kaplan–Meier estimator; bias; censoring; least squares; linear regression; Kaplan-Meier estimator},
language = {eng},
number = {5},
pages = {1045-1063},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Bias correction on censored least squares regression models},
url = {http://eudml.org/doc/251404},
volume = {48},
year = {2012},
}

TY - JOUR
AU - Orbe, Jesus
AU - Núñez-Antón, Vicente
TI - Bias correction on censored least squares regression models
JO - Kybernetika
PY - 2012
PB - Institute of Information Theory and Automation AS CR
VL - 48
IS - 5
SP - 1045
EP - 1063
AB - This paper proposes a bias reduction of the coefficients' estimator for linear regression models when observations are randomly censored and the error distribution is unknown. The proposed bias correction is applied to the weighted least squares estimator proposed by Stute [28] [W. Stute: Consistent estimation under random censorship when covariables are present. J. Multivariate Anal. 45 (1993), 89-103.], and it is based on model-based bootstrap resampling techniques that also allow us to work with censored data. Our bias-corrected estimator proposal is evaluated and its behavior assessed in simulation studies concluding that both the bias and the mean square error are reduced with the new proposal.
LA - eng
KW - bias; censoring; least squares; linear regression; Kaplan–Meier estimator; bias; censoring; least squares; linear regression; Kaplan-Meier estimator
UR - http://eudml.org/doc/251404
ER -

References

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