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Transform both sides model: A parametric approach

Author

Listed:
  • Polpo, A.
  • de Campos, C.P.
  • Sinha, D.
  • Lipsitz, S.
  • Lin, J.

Abstract

A parametric regression model for right-censored data with a log-linear median regression function and a transformation in both response and regression parts, named parametric Transform-Both-Sides (TBS) model, is presented. The TBS model has a parameter that handles data asymmetry while allowing various different distributions for the error, as long as they are unimodal symmetric distributions centered at zero. The discussion is focused on the estimation procedure with five important error distributions (normal, double-exponential, Student’s t, Cauchy and logistic) and presents properties, associated functions (that is, survival and hazard functions) and estimation methods based on maximum likelihood and on the Bayesian paradigm. These procedures are implemented in TBSSurvival, an open-source fully documented R package. The use of the package is illustrated and the performance of the model is analyzed using both simulated and real data sets.

Suggested Citation

  • Polpo, A. & de Campos, C.P. & Sinha, D. & Lipsitz, S. & Lin, J., 2014. "Transform both sides model: A parametric approach," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 903-913.
  • Handle: RePEc:eee:csdana:v:71:y:2014:i:c:p:903-913
    DOI: 10.1016/j.csda.2013.07.023
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    References listed on IDEAS

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    1. BuHamra, Sana S. & Al-Kandari, N.M.Noriah M. & Ahmed, S. E., 2004. "Inference concerning quantile for left truncated and right censored data," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 819-831, July.
    2. Bernd Fitzenberger & Ralf A. Wilke & Xuan Zhang, 2010. "Implementing Box-Cox Quantile Regression," Econometric Reviews, Taylor & Francis Journals, vol. 29(2), pages 158-181, April.
    3. Jianchang Lin & Debajyoti Sinha & Stuart Lipsitz & Adriano Polpo, 2012. "Semiparametric Bayesian Survival Analysis using Models with Log-linear Median," Biometrics, The International Biometric Society, vol. 68(4), pages 1136-1145, December.
    4. Portnoy S., 2003. "Censored Regression Quantiles," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 1001-1012, January.
    5. Peng, Limin & Huang, Yijian, 2008. "Survival Analysis With Quantile Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 637-649, June.
    6. Fitzmaurice, Garrett M. & Lipsitz, Stuart R. & Parzen, Michael, 2007. "Approximate Median Regression via the Box-Cox Transformation," The American Statistician, American Statistical Association, vol. 61, pages 233-238, August.
    7. Lin, Guixian & He, Xuming & Portnoy, Stephen, 2012. "Quantile regression with doubly censored data," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 797-812.
    8. Koenker, Roger, 2008. "Censored Quantile Regression Redux," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i06).
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