Shrinkage and Pretest Nonparametric Estimation of Regression Parameters from Censored Data with Multiple Observations at Each Level of Covariate

DOI<link itemprop=identifier href="https://doi.org/10.1002/1521-4036(200008)42:4<511::AID-BIMJ511>3.0.CO;2-I" /><511::AID-BIMJ511>3.0.CO;2-I 10.1002/1521-4036(200008)42:4<511::AID-BIMJ511>3.0.CO;2-IzbMath0959.62037OpenAlexW2005333656MaRDI QIDQ4518275

Mohammad H. Rahbar, S. Ejaz Ahmed

Publication date: 2 May 2001

Full work available at URL: https://doi.org/10.1002/1521-4036(200008)42:4<511::aid-bimj511>3.0.co;2-i

zbMATH Keywords

simulationasymptotic biasclinical trialslocal alternativesright censored datarestricted estimatorpretest estimatormean squared errors

Mathematics Subject Classification ID

Nonparametric regression and quantile regression (62G08) Asymptotic properties of nonparametric inference (62G20) Estimation in survival analysis and censored data (62N02)

Related Items (1)

LASSO and shrinkage estimation in Weibull censored regression models

Cites Work

• Consistency results for linear regression with censored data
• Regression analysis with randomly right-censored data
• Nonparametric estimation of regression parameters from censored data with a discrete covariate
• Linear Models, Random Censoring and Synthetic Data
• Inference based on conditional speclfication
• Linear regression with censored data
• Least squares regression with censored data
• On Biases in Estimation Due to the Use of Preliminary Tests of Significance

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