Bias correction on censored least squares regression models

Title:

Bias correction on censored least squares regression models

Creator:

Orbe, Jesus and Núñez-Antón, Vicente

Identifier:

https://cdk.lib.cas.cz/client/handle/uuid:a3dc4250-0109-40ef-971a-dd024e7ff56c
uuid:a3dc4250-0109-40ef-971a-dd024e7ff56c

Subject:

bias, censoring, least squares, linear regression, and Kaplan-Mayer estimator

Type:

model:article and TEXT

Format:

bez média and svazek

Description:

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. \emph{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.

Language:

English

Rights:

http://creativecommons.org/publicdomain/mark/1.0/
policy:public

Coverage:

[1045]-1063

Source:

Kybernetika | 2012 Volume:48 | Number:5

Harvested from:

CDK

Metadata only:

false