IDEAS home Printed from https://ideas.repec.org/a/bla/scjsta/v43y2016i2p558-572.html
   My bibliography  Save this article

Locally Efficient Semiparametric Estimators for Proportional Hazards Models with Measurement Error

Author

Listed:
  • Yuhang Xu
  • Yehua Li
  • Xiao Song

Abstract

No abstract is available for this item.

Suggested Citation

  • Yuhang Xu & Yehua Li & Xiao Song, 2016. "Locally Efficient Semiparametric Estimators for Proportional Hazards Models with Measurement Error," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(2), pages 558-572, June.
    • Handle: RePEc:bla:scjsta:v:43:y:2016:i:2:p:558-572
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/sjos.12191
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Cheng, Yu-Jen & Crainiceanu, Ciprian M., 2009. "Cox Models With Smooth Functional Effect of Covariates Measured With Error," Journal of the American Statistical Association, American Statistical Association, vol. 104(487), pages 1144-1154.
    2. Xiao Song & Yijian Huang, 2005. "On Corrected Score Approach for Proportional Hazards Model with Covariate Measurement Error," Biometrics, The International Biometric Society, vol. 61(3), pages 702-714, September.
    3. Ma, Yanyuan & Carroll, Raymond J., 2006. "Locally Efficient Estimators for Semiparametric Models With Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1465-1474, December.
    4. Anastasios A. Tsiatis & Yanyuan Ma, 2004. "Locally efficient semiparametric estimators for functional measurement error models," Biometrika, Biometrika Trust, vol. 91(4), pages 835-848, December.
    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. Yanyuan Ma & Jeffrey D. Hart & Ryan Janicki & Raymond J. Carroll, 2011. "Local and omnibus goodness‐of‐fit tests in classical measurement error models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(1), pages 81-98, January.
    2. Jun Zhang & Zhenghui Feng & Peirong Xu & Hua Liang, 2017. "Generalized varying coefficient partially linear measurement errors models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(1), pages 97-120, February.
    3. Garcia, Tanya P. & Ma, Yanyuan, 2017. "Simultaneous treatment of unspecified heteroskedastic model error distribution and mismeasured covariates for restricted moment models," Journal of Econometrics, Elsevier, vol. 200(2), pages 194-206.
    4. Qianqian Wang & Yanyuan Ma & Guangren Yang, 2020. "Locally efficient estimation in generalized partially linear model with measurement error in nonlinear function," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 29(2), pages 553-572, June.
    5. Mijeong Kim & Yanyuan Ma, 2012. "The efficiency of the second-order nonlinear least squares estimator and its extension," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(4), pages 751-764, August.
    6. Sun, Zhihua & Ye, Xue & Sun, Liuquan, 2015. "Consistent test of error-in-variables partially linear model with auxiliary variables," Journal of Multivariate Analysis, Elsevier, vol. 141(C), pages 118-131.
    7. Yanyuan Ma & Marc G. Genton, 2010. "Explicit estimating equations for semiparametric generalized linear latent variable models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 72(4), pages 475-495, September.
    8. Li, Mengyan & Ma, Yanyuan & Li, Runze, 2019. "Semiparametric regression for measurement error model with heteroscedastic error," Journal of Multivariate Analysis, Elsevier, vol. 171(C), pages 320-338.
    9. Fei Jiang & Sebastien Haneuse, 2017. "A Semi-parametric Transformation Frailty Model for Semi-competing Risks Survival Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 44(1), pages 112-129, March.
    10. Jingxuan Luo & Lili Yue & Gaorong Li, 2023. "Overview of High-Dimensional Measurement Error Regression Models," Mathematics, MDPI, vol. 11(14), pages 1-22, July.
    11. Paola Berchialla & Veronica Sciannameo & Sara Urru & Corrado Lanera & Danila Azzolina & Dario Gregori & Ileana Baldi, 2021. "Adjustment for Baseline Covariates to Increase Efficiency in RCTs with Binary Endpoint: A Comparison of Bayesian and Frequentist Approaches," IJERPH, MDPI, vol. 18(15), pages 1-9, July.
    12. Li-Pang Chen & Grace Y. Yi, 2021. "Semiparametric methods for left-truncated and right-censored survival data with covariate measurement error," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 73(3), pages 481-517, June.
    13. Yanqing Sun & Qingning Zhou & Peter B. Gilbert, 2023. "Analysis of the Cox Model with Longitudinal Covariates with Measurement Errors and Partly Interval Censored Failure Times, with Application to an AIDS Clinical Trial," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 430-454, July.
    14. Stoklosa, Jakub & Huang, Yih-Huei & Furlan, Elise & Hwang, Wen-Han, 2016. "On quadratic logistic regression models when predictor variables are subject to measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 95(C), pages 109-121.
    15. Kun Xu & Yanyuan Ma & Liqun Wang, 2015. "Instrument Assisted Regression for Errors in Variables Models with Binary Response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(1), pages 104-117, March.
    16. Tanya P. Garcia & Yanyuan Ma, 2016. "Optimal Estimator for Logistic Model with Distribution-free Random Intercept," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(1), pages 156-171, March.
    17. Ying Yan & Grace Y. Yi, 2016. "A Class of Functional Methods for Error-Contaminated Survival Data Under Additive Hazards Models with Replicate Measurements," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 684-695, April.
    18. Yih-Huei Huang & Chi-Chung Wen & Yu-Hua Hsu, 2015. "The Extensively Corrected Score for Measurement Error Models," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 42(4), pages 911-924, December.
    19. Grace Y. Yi & Yanyuan Ma & Donna Spiegelman & Raymond J. Carroll, 2015. "Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 681-696, June.
    20. Pamela A. Shaw & Ross L. Prentice, 2012. "Hazard Ratio Estimation for Biomarker-Calibrated Dietary Exposures," Biometrics, The International Biometric Society, vol. 68(2), pages 397-407, 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:bla:scjsta:v:43:y:2016:i:2:p:558-572. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0303-6898 .

    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.