IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v66y2010i2p415-425.html
   My bibliography  Save this article

Regression Analysis with a Misclassified Covariate from a Current Status Observation Scheme

Author

Listed:
  • Leilei Zeng
  • Richard J. Cook
  • Theodore E. Warkentin

Abstract

No abstract is available for this item.

Suggested Citation

  • Leilei Zeng & Richard J. Cook & Theodore E. Warkentin, 2010. "Regression Analysis with a Misclassified Covariate from a Current Status Observation Scheme," Biometrics, The International Biometric Society, vol. 66(2), pages 415-425, June.
    • Handle: RePEc:bla:biomet:v:66:y:2010:i:2:p:415-425
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2009.01299.x
    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. K. F. Lam & Hongqi Xue, 2005. "A semiparametric regression cure model with current status data," Biometrika, Biometrika Trust, vol. 92(3), pages 573-586, September.
    2. J. F. Lawless & J. D. Kalbfleisch & C. J. Wild, 1999. "Semiparametric methods for response‐selective and missing data problems in regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(2), pages 413-438, April.
    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. Esmerelda A. Ramalho & Richard Smith, 2003. "Discrete choice non-response," CeMMAP working papers 07/03, Institute for Fiscal Studies.
    2. Ryo Kato & Takahiro Hoshino, 2020. "Semiparametric Bayesian multiple imputation for regression models with missing mixed continuous–discrete covariates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 72(3), pages 803-825, June.
    3. Aubry, Philippe & Francesiaz, Charlotte & Guillemain, Matthieu, 2024. "On the impact of preferential sampling on ecological status and trend assessment," Ecological Modelling, Elsevier, vol. 492(C).
    4. Guoqing Diao & Ao Yuan, 2019. "A class of semiparametric cure models with current status data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 25(1), pages 26-51, January.
    5. Zhiwei Zhang & Howard Rockette, 2006. "Semiparametric Maximum Likelihood for Missing Covariates in Parametric Regression," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 58(4), pages 687-706, December.
    6. Jonathan S. Schildcrout & Shawn P. Garbett & Patrick J. Heagerty, 2013. "Outcome Vector Dependent Sampling with Longitudinal Continuous Response Data: Stratified Sampling Based on Summary Statistics," Biometrics, The International Biometric Society, vol. 69(2), pages 405-416, June.
    7. J. F. Lawless, 2018. "Two-phase outcome-dependent studies for failure times and testing for effects of expensive covariates," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(1), pages 28-44, January.
    8. Hoshino, Takahiro, 2008. "A Bayesian propensity score adjustment for latent variable modeling and MCMC algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1413-1429, January.
    9. Brady Ryan & Ananthika Nirmalkanna & Candemir Cigsar & Yildiz E. Yilmaz, 2023. "Evaluation of Designs and Estimation Methods Under Response-Dependent Two-Phase Sampling for Genetic Association Studies," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(2), pages 510-539, July.
    10. Haibo Zhou & Rui Song & Yuanshan Wu & Jing Qin, 2011. "Statistical Inference for a Two-Stage Outcome-Dependent Sampling Design with a Continuous Outcome," Biometrics, The International Biometric Society, vol. 67(1), pages 194-202, March.
    11. Takahiro Hoshino & Hiroshi Kurata & Kazuo Shigemasu, 2006. "A Propensity Score Adjustment for Multiple Group Structural Equation Modeling," Psychometrika, Springer;The Psychometric Society, vol. 71(4), pages 691-712, December.
    12. Hu, Tao & Xiang, Liming, 2016. "Partially linear transformation cure models for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 257-269.
    13. Li, Shuwei & Hu, Tao & Zhao, Xingqiu & Sun, Jianguo, 2019. "A class of semiparametric transformation cure models for interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 133(C), pages 153-165.
    14. Sasaki, Yuya & Ura, Takuya, 2023. "Estimation and inference for policy relevant treatment effects," Journal of Econometrics, Elsevier, vol. 234(2), pages 394-450.
    15. Xiaofei Wang & Haibo Zhou, 2006. "A Semiparametric Empirical Likelihood Method for Biased Sampling Schemes with Auxiliary Covariates," Biometrics, The International Biometric Society, vol. 62(4), pages 1149-1160, December.
    16. Liang, Hua, 2008. "Generalized partially linear models with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 99(5), pages 880-895, May.
    17. Yang Zhao & Meng Liu, 2021. "Unified approach for regression models with nonmonotone missing at random data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 105(1), pages 87-101, March.
    18. Fatema Shafie Khorassani & Jeremy M. G. Taylor & Niko Kaciroti & Michael R. Elliott, 2023. "Incorporating Covariates into Measures of Surrogate Paradox Risk," Stats, MDPI, vol. 6(1), pages 1-23, February.
    19. Esmeralda A. Ramalho & Richard J. Smith, 2013. "Discrete Choice Non-Response," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 80(1), pages 343-364.
    20. Xue Yuan & Wang Jinjuan & Ding Juan & Zhang Sanguo & Li Qizhai, 2019. "A powerful test for ordinal trait genetic association analysis," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 18(2), pages 1-9, April.

    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:biomet:v:66:y:2010:i:2:p:415-425. 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=0006-341X .

    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.