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A comparative study of nonparametric estimation in Weibull regression: A penalized likelihood approach

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  • Kim, Young-Ju

Abstract

The Weibull distribution is popularly used to model lifetime distributions in many areas of applied statistics. This paper employs a penalized likelihood method to estimate the shape parameter and an unknown regression function simultaneously in a nonparametric Weibull regression. Four methods were considered: two cross-validation methods, a corrected Akaike information criterion, and a Bayesian information criterion. Each method was evaluated based on shape parameter estimation as well as selecting the smoothing parameter in a penalized likelihood model through a simulation study. Adapting a lower-dimensional approximation and deriving confidence intervals from Bayes models of the penalized likelihood, the comparative performances of methods using both censored and uncensored data were examined for various censoring rates. The methods are applied to a real data example of lung cancer.

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  • Kim, Young-Ju, 2011. "A comparative study of nonparametric estimation in Weibull regression: A penalized likelihood approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1884-1896, April.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:4:p:1884-1896
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    1. Simon N. Wood, 2008. "Fast stable direct fitting and smoothness selection for generalized additive models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(3), pages 495-518, July.
    2. Young‐Ju Kim & Chong Gu, 2004. "Smoothing spline Gaussian regression: more scalable computation via efficient approximation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(2), pages 337-356, May.
    3. Lee, Thomas C. M., 2003. "Smoothing parameter selection for smoothing splines: a simulation study," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 139-148, February.
    4. Du, Pang & Gu, Chong, 2006. "Penalized likelihood hazard estimation: Efficient approximation and Bayesian confidence intervals," Statistics & Probability Letters, Elsevier, vol. 76(3), pages 244-254, February.
    5. Malloy, Elizabeth J. & Spiegelman, Donna & Eisen, Ellen A., 2009. "Comparing measures of model selection for penalized splines in Cox models," Computational Statistics & Data Analysis, Elsevier, vol. 53(7), pages 2605-2616, May.
    6. Clifford M. Hurvich & Jeffrey S. Simonoff & Chih‐Ling Tsai, 1998. "Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 60(2), pages 271-293.
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