IDEAS home Printed from https://ideas.repec.org/p/bep/hvdbio/1006.html
   My bibliography  Save this paper

Semi-parametric Box-Cox Power Transformation Models for Censored Survival Observations

Author

Listed:
  • Tianxi Cai

    (Harvard University)

  • Lu Tian

    (Harvard University)

  • L. J. Wei

    (Harvard University)

Abstract

No abstract is available for this item.

Suggested Citation

  • Tianxi Cai & Lu Tian & L. J. Wei, 2004. "Semi-parametric Box-Cox Power Transformation Models for Censored Survival Observations," Harvard University Biostatistics Working Paper Series 1006, Berkeley Electronic Press.
    • Handle: RePEc:bep:hvdbio:1006
    Note: oai:bepress.com:harvardbiostat-1006
    as

    Download full text from publisher

    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1006&context=harvardbiostat
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhezhen Jin, 2003. "Rank-based inference for the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(2), pages 341-353, June.
    2. D. Y. Lin & L. J. Wei & Z. Ying, 2002. "Model-Checking Techniques Based on Cumulative Residuals," Biometrics, The International Biometric Society, vol. 58(1), pages 1-12, March.
    3. Newey, Whitney K, 1990. "Efficient Instrumental Variables Estimation of Nonlinear Models," Econometrica, Econometric Society, vol. 58(4), pages 809-837, July.
    4. Han, Aaron K., 1987. "A non-parametric analysis of transformations," Journal of Econometrics, Elsevier, vol. 35(2-3), pages 191-209, July.
    5. Yuhyun Park, 2003. "Estimating subject-specific survival functions under the accelerated failure time model," Biometrika, Biometrika Trust, vol. 90(3), pages 717-723, September.
    6. Honore, Bo E. & Powell, James L., 1994. "Pairwise difference estimators of censored and truncated regression models," Journal of Econometrics, Elsevier, vol. 64(1-2), pages 241-278.
    7. Robinson, P M, 1991. "Best Nonlinear Three-Stage Least Squares Estimation of Certain Econometric Models," Econometrica, Econometric Society, vol. 59(3), pages 755-786, May.
    8. Foster A. M. & Tian L. & Wei L. J., 2001. "Estimation for the Box-Cox Transformation Model Without Assuming Parametric Error Distribution," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1097-1101, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.
    2. Linton, Oliver, 1995. "Second Order Approximation in the Partially Linear Regression Model," Econometrica, Econometric Society, vol. 63(5), pages 1079-1112, September.
    3. Hahn, Jinyong, 1997. "Efficient estimation of panel data models with sequential moment restrictions," Journal of Econometrics, Elsevier, vol. 79(1), pages 1-21, July.
    4. Poirier, Alexandre, 2017. "Efficient estimation in models with independence restrictions," Journal of Econometrics, Elsevier, vol. 196(1), pages 1-22.
    5. Chen, Xiaohong & Linton, Oliver & Jacho-Chávez, David T., 2009. "An alternative way of computing efficient instrumental variable estimators," LSE Research Online Documents on Economics 58016, London School of Economics and Political Science, LSE Library.
    6. Chamberlain, Gary, 2022. "Feedback in panel data models," Journal of Econometrics, Elsevier, vol. 226(1), pages 4-20.
    7. Pascual, Lorenzo & Romo, Juan & Ruiz, Esther, 2005. "Bootstrap prediction intervals for power-transformed time series," International Journal of Forecasting, Elsevier, vol. 21(2), pages 219-235.
    8. Marazzi, Alfio & Yohai, Victor J., 2006. "Robust Box-Cox transformations based on minimum residual autocorrelation," Computational Statistics & Data Analysis, Elsevier, vol. 50(10), pages 2752-2768, June.
    9. Fan, Caiyun & Lu, Wenbin & Zhou, Yong, 2021. "Testing error heterogeneity in censored linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 161(C).
    10. Oliver Linton, 1997. "Second Order Approximation in a Linear Regression with Heteroskedasticity for Unknown Form," Cowles Foundation Discussion Papers 1151, Cowles Foundation for Research in Economics, Yale University.
    11. Calzolari, Giorgio, 1992. "Stima delle equazioni simultanee non-lineari: una rassegna [Estimation of nonlinear simultaneous equations: a survey]," MPRA Paper 24123, University Library of Munich, Germany, revised 1992.
    12. A. Adam Ding & Guangkai Shi & Weijing Wang & Jin‐Jian Hsieh, 2009. "Marginal Regression Analysis for Semi‐Competing Risks Data Under Dependent Censoring," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 36(3), pages 481-500, September.
    13. Arkadiusz Szydlowski, 2017. "Testing a parametric transformation model versus a nonparametric alternative," Discussion Papers in Economics 17/15, Division of Economics, School of Business, University of Leicester.
    14. Weibin Zhong & Guoqing Diao, 2023. "Semiparametric Density Ratio Model for Survival Data with a Cure Fraction," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 217-241, April.
    15. Calzolari, Giorgio & Fiorentini, Gabriele, 1994. "Conditional heteroskedasticity in nonlinear simultaneous equations," MPRA Paper 24428, University Library of Munich, Germany.
    16. Hansen, Lars Peter, 2013. "Uncertainty Outside and Inside Economic Models," Nobel Prize in Economics documents 2013-7, Nobel Prize Committee.
    17. Menzel, Konrad, 2014. "Consistent estimation with many moment inequalities," Journal of Econometrics, Elsevier, vol. 182(2), pages 329-350.
    18. Ichimura, Hidehiko & Thompson, T. Scott, 1998. "Maximum likelihood estimation of a binary choice model with random coefficients of unknown distribution," Journal of Econometrics, Elsevier, vol. 86(2), pages 269-295, June.
    19. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    20. Lili Yu & Liang Liu & Ding-Geng(Din) Chen, 2013. "Weighted Least-Squares Method for Right-Censored Data in Accelerated Failure Time Model," Biometrics, The International Biometric Society, vol. 69(2), pages 358-365, June.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bep:hvdbio:1006. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Christopher F. Baum (email available below). General contact details of provider: http://www.bepress.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.