IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v102y2011i1p182-192.html
   My bibliography  Save this article

Robust inference for sparse cluster-correlated count data

Author

Listed:
  • Hanfelt, John J.
  • Li, Ruosha
  • Pan, Yi
  • Payment, Pierre

Abstract

Standard methods for the analysis of cluster-correlated count data fail to yield valid inferences when the study is finely stratified and the interest is in assessing the intracluster correlation structure. We present an approach, based upon exactly adjusting an estimating function for the bias induced by the fitting of stratum-specific effects, that requires modeling only the first two joint moments of the observations and that yields consistent and asymptotically normal estimators of the correlation parameters.

Suggested Citation

  • Hanfelt, John J. & Li, Ruosha & Pan, Yi & Payment, Pierre, 2011. "Robust inference for sparse cluster-correlated count data," Journal of Multivariate Analysis, Elsevier, vol. 102(1), pages 182-192, January.
    • Handle: RePEc:eee:jmvana:v:102:y:2011:i:1:p:182-192
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(10)00190-9
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    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. Huang Y. & Wang C.Y., 2001. "Consistent Functional Methods for Logistic Regression With Errors in Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1469-1482, December.
    2. John J. Hanfelt, 2003. "Conditioning to reduce the sensitivity of general estimating functions to nuisance parameters," Biometrika, Biometrika Trust, vol. 90(3), pages 517-531, September.
    3. Thomas A. Severini, 2002. "Modified estimating functions," Biometrika, Biometrika Trust, vol. 89(2), pages 333-343, June.
    4. John J. Hanfelt, 2004. "Composite conditional likelihood for sparse clustered data," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 259-273, February.
    5. Molin Wang, 2003. "Adjusted profile estimating function," Biometrika, Biometrika Trust, vol. 90(4), pages 845-858, December.
    6. Molin Wang & John J. Hanfelt, 2007. "Orthogonal locally ancillary estimating functions for matched pair studies and errors in covariates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(3), pages 411-428, June.
    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. Stefano Cabras & María Castellanos & Erlis Ruli, 2014. "A Quasi likelihood approximation of posterior distributions for likelihood-intractable complex models," METRON, Springer;Sapienza Università di Roma, vol. 72(2), pages 153-167, August.
    2. Di Shu & Grace Y. Yi, 2018. "Estimation of Causal Effect Measures in the Presence of Measurement Error in Confounders," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 10(1), pages 233-254, April.
    3. Bhat, Chandra R. & Sener, Ipek N. & Eluru, Naveen, 2010. "A flexible spatially dependent discrete choice model: Formulation and application to teenagers' weekday recreational activity participation," Transportation Research Part B: Methodological, Elsevier, vol. 44(8-9), pages 903-921, September.
    4. Zanhua Yin, 2020. "Variable selection for sparse logistic regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(7), pages 821-836, October.
    5. Li, Tong & Hsiao, Cheng, 2004. "Robust estimation of generalized linear models with measurement errors," Journal of Econometrics, Elsevier, vol. 118(1-2), pages 51-65.
    6. Qin, Guoyou & Zhang, Jiajia & Zhu, Zhongyi, 2016. "Simultaneous mean and covariance estimation of partially linear models for longitudinal data with missing responses and covariate measurement error," Computational Statistics & Data Analysis, Elsevier, vol. 96(C), pages 24-39.
    7. Weiming Yang & Yiping Yang, 2020. "Composite quantile regression estimation of linear error-in-variable models using instrumental variables," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(1), pages 1-16, January.
    8. F. Bartolucci & R. Bellio & A. Salvan & N. Sartori, 2016. "Modified Profile Likelihood for Fixed-Effects Panel Data Models," Econometric Reviews, Taylor & Francis Journals, vol. 35(7), pages 1271-1289, August.
    9. Nels G. Johnson & Inyoung Kim, 2019. "Semiparametric approaches for matched case–control studies with error-in-covariates," Computational Statistics, Springer, vol. 34(4), pages 1675-1692, December.
    10. Molin Wang & John M. Williamson, 2005. "Generalization of the Mantel–Haenszel Estimating Function for Sparse Clustered Binary Data," Biometrics, The International Biometric Society, vol. 61(4), pages 973-981, December.
    11. Jun Yan & Jian Huang, 2009. "Partly Functional Temporal Process Regression with Semiparametric Profile Estimating Functions," Biometrics, The International Biometric Society, vol. 65(2), pages 431-440, June.
    12. Manuel Arellano & Jinyong Hahn, 2005. "Understanding Bias in Nonlinear Panel Models: Some Recent Developments," Working Papers wp2005_0507, CEMFI.
    13. Laurence S. Freedman & Vitaly Fainberg & Victor Kipnis & Douglas Midthune & Raymond J. Carroll, 2004. "A New Method for Dealing with Measurement Error in Explanatory Variables of Regression Models," Biometrics, The International Biometric Society, vol. 60(1), pages 172-181, March.
    14. Cheng Zheng & Yingye Zheng, 2019. "Calibrating Variations in Biomarker Measures for Improving Prediction with Time-to-event Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(3), pages 477-503, December.
    15. Cristiano Varin, 2008. "On composite marginal likelihoods," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(1), pages 1-28, February.
    16. Xiao Song & Yijian Huang, 2004. "On Corrected Score Approach for Proportional Hazards Model with Covariate Measurement Error," UW Biostatistics Working Paper Series 1058, Berkeley Electronic Press.
    17. Lu Lin & Lixing Zhu & K. Yuen, 2005. "Profile empirical likelihood for parametric and semiparametric models," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 57(3), pages 485-505, September.

    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:eee:jmvana:v:102:y:2011:i:1:p:182-192. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.