IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v102y2015i4p907-923..html
   My bibliography  Save this article

Diagnostic measures for the Cox regression model with missing covariates

Author

Listed:
  • Hongtu Zhu
  • Joseph G. Ibrahim
  • Ming-Hui Chen

Abstract

We investigate diagnostic measures for assessing the influence of observations and model misspecification on the Cox regression model when there are missing covariate data. Our diagnostics include case-deletion measures, conditional martingale residuals, and score residuals. The Q-distance is introduced to examine the effects of deleting individual observations on the estimates of finite- and infinite-dimensional parameters. Conditional martingale residuals are used to construct goodness-of-fit statistics for testing misspecification of the model assumptions. A resampling method is developed to approximate the $p$-values of the goodness-of-fit statistics. We conduct simulation studies to evaluate our methods, and analyse a real dataset to illustrate their use.

Suggested Citation

  • Hongtu Zhu & Joseph G. Ibrahim & Ming-Hui Chen, 2015. "Diagnostic measures for the Cox regression model with missing covariates," Biometrika, Biometrika Trust, vol. 102(4), pages 907-923.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:4:p:907-923.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asv047
    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. Escanciano, J. Carlos, 2006. "A Consistent Diagnostic Test For Regression Models Using Projections," Econometric Theory, Cambridge University Press, vol. 22(6), pages 1030-1051, 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. Pedro H. C. Sant'Anna & Xiaojun Song & Qi Xu, 2022. "Covariate distribution balance via propensity scores," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(6), pages 1093-1120, September.
    2. Wang, Xuexin, 2015. "A Note on Consistent Conditional Moment Tests," MPRA Paper 69005, University Library of Munich, Germany.
    3. Escanciano, Juan Carlos & Jacho-Chávez, David T., 2010. "Approximating the critical values of Cramér-von Mises tests in general parametric conditional specifications," Computational Statistics & Data Analysis, Elsevier, vol. 54(3), pages 625-636, March.
    4. Juan Carlos Escanciano & Kyungchul Song, 2007. "Asymptotically Optimal Tests for Single-Index Restrictions with a Focus on Average Partial Effects," PIER Working Paper Archive 07-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania.
    5. Wasel Shadat, 2011. "On the Nonparametric Tests of Univariate GARCH Regression Models," Economics Discussion Paper Series 1115, Economics, The University of Manchester.
    6. Pascal Lavergne & Valentin Patilea, 2011. "One for All and All for One: Regression Checks With Many Regressors," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 30(1), pages 41-52, January.
    7. Eduardo García‐Portugués & Javier Álvarez‐Liébana & Gonzalo Álvarez‐Pérez & Wenceslao González‐Manteiga, 2021. "A goodness‐of‐fit test for the functional linear model with functional response," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(2), pages 502-528, June.
    8. Kunyang Song & Feiyu Jiang & Ke Zhu, 2024. "Estimation for conditional moment models based on martingale difference divergence," Papers 2404.11092, arXiv.org.
    9. Junmin Liu & Deli Zhu & Luoyao Yu & Xuehu Zhu, 2023. "Specification testing of partially linear single-index models: a groupwise dimension reduction-based adaptive-to-model approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 32(1), pages 232-262, March.
    10. Pedro H. C. Sant'Anna, 2016. "Program Evaluation with Right-Censored Data," Papers 1604.02642, arXiv.org.
    11. Song, Kyungchul, 2010. "Testing semiparametric conditional moment restrictions using conditional martingale transforms," Journal of Econometrics, Elsevier, vol. 154(1), pages 74-84, January.
    12. Lavergne, Pascal & Patilea, Valentin, 2008. "Breaking the curse of dimensionality in nonparametric testing," Journal of Econometrics, Elsevier, vol. 143(1), pages 103-122, March.
    13. James Davidson & Andreea G. Halunga, 2013. "Consistent Model Specification Testing," Discussion Papers 1312, University of Exeter, Department of Economics.
    14. Colling, Benjamin & Van Keilegom, Ingrid, 2016. "Goodness-of-fit tests in semiparametric transformation models using the integrated regression function," LIDAM Discussion Papers ISBA 2016031, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    15. Juan Carlos Escanciano & Joel Robert Terschuur, 2022. "Machine Learning Inference on Inequality of Opportunity," Papers 2206.05235, arXiv.org, revised Oct 2023.
    16. Chen, Feifei & Jiang, Qing & Feng, Zhenghui & Zhu, Lixing, 2020. "Model checks for functional linear regression models based on projected empirical processes," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    17. Lapenta, Elia & Lavergne, Pascal, 2022. "Encompassing Tests for Nonparametric Regressions," TSE Working Papers 22-1332, Toulouse School of Economics (TSE).
    18. Manuel A. Domínguez & Ignacio N. Lobato, 2020. "Specification testing with estimated variables," Econometric Reviews, Taylor & Francis Journals, vol. 39(5), pages 476-494, May.
    19. Ana Pérez-González & Tomás R. Cotos-Yáñez & Wenceslao González-Manteiga & Rosa M. Crujeiras-Casais, 2021. "Goodness-of-fit tests for quantile regression with missing responses," Statistical Papers, Springer, vol. 62(3), pages 1231-1264, June.
    20. Yasser Al Zaim & Mohammad Reza Faridrohani, 2021. "Bayesian random projection-based signal detection for Gaussian scale space random fields," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(3), pages 503-532, September.

    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:oup:biomet:v:102:y:2015:i:4:p:907-923.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.