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Shrinkage estimation in lognormal regression model for censored data

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  • Shakhawat Hossain
  • Hatem A. Howlader

Abstract

We introduce in this paper, the shrinkage estimation method in the lognormal regression model for censored data involving many predictors, some of which may not have any influence on the response of interest. We develop the asymptotic properties of the shrinkage estimators (SEs) using the notion of asymptotic distributional biases and risks. We show that if the shrinkage dimension exceeds two, the asymptotic risk of the SEs is strictly less than the corresponding classical estimators. Furthermore, we study the penalty (LASSO and adaptive LASSO) estimation methods and compare their relative performance with the SEs. A simulation study for various combinations of the inactive predictors and censoring percentages shows that the SEs perform better than the penalty estimators in certain parts of the parameter space, especially when there are many inactive predictors in the model. It also shows that the shrinkage and penalty estimators outperform the classical estimators. A real-life data example using Worcester heart attack study is used to illustrate the performance of the suggested estimators.

Suggested Citation

  • Shakhawat Hossain & Hatem A. Howlader, 2017. "Shrinkage estimation in lognormal regression model for censored data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(1), pages 162-180, January.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:1:p:162-180
    DOI: 10.1080/02664763.2016.1168365
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    References listed on IDEAS

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    1. Jian Huang & Shuangge Ma & Huiliang Xie, 2006. "Regularized Estimation in the Accelerated Failure Time Model with High-Dimensional Covariates," Biometrics, The International Biometric Society, vol. 62(3), pages 813-820, September.
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    Cited by:

    1. Shakhawat Hossain & Shahedul A. Khan, 2020. "Shrinkage estimation of the exponentiated Weibull regression model for time‐to‐event data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 74(4), pages 592-610, November.

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