IDEAS home Printed from https://ideas.repec.org/a/bla/biomet/v69y2013i1p80-90.html
   My bibliography  Save this article

A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error

Author

Listed:
  • David M. Zucker
  • Malka Gorfine
  • Yi Li
  • Mahlet G. Tadesse
  • Donna Spiegelman

Abstract

No abstract is available for this item.

Suggested Citation

  • David M. Zucker & Malka Gorfine & Yi Li & Mahlet G. Tadesse & Donna Spiegelman, 2013. "A Regularization Corrected Score Method for Nonlinear Regression Models with Covariate Error," Biometrics, The International Biometric Society, vol. 69(1), pages 80-90, March.
    • Handle: RePEc:bla:biomet:v:69:y:2013:i:1:p:80-90
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1111/j.1541-0420.2012.01833.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. Sylvia Richardson & Laurent Leblond & Isabelle Jaussent & Peter J. Green, 2002. "Mixture models in measurement error problems, with reference to epidemiological studies," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(3), pages 549-566, October.
    2. Gallant, A Ronald & Nychka, Douglas W, 1987. "Semi-nonparametric Maximum Likelihood Estimation," Econometrica, Econometric Society, vol. 55(2), pages 363-390, March.
    3. Buzas, Jeffrey S., 2009. "A note on corrected scores for logistic regression," Statistics & Probability Letters, Elsevier, vol. 79(22), pages 2351-2358, November.
    4. Devanarayan, Viswanath & Stefanski, Leonard A., 2002. "Empirical simulation extrapolation for measurement error models with replicate measurements," Statistics & Probability Letters, Elsevier, vol. 59(3), pages 219-225, October.
    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. Anders Skrondal & Sophia Rabe‐Hesketh, 2007. "Latent Variable Modelling: A Survey," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 34(4), pages 712-745, December.
    2. Ho, Remus K.W. & Hu, Inchi, 2008. "Flexible modelling of random effects in linear mixed models--A Bayesian approach," Computational Statistics & Data Analysis, Elsevier, vol. 52(3), pages 1347-1361, January.
    3. Pieter J. van der Sluis, 1997. "Post-Sample Prediction Tests for the Efficient Method of Moments," Tinbergen Institute Discussion Papers 97-054/4, Tinbergen Institute.
    4. Cooper, Joseph C., 2002. "Flexible Functional Form Estimation of Willingness to Pay Using Dichotomous Choice Data," Journal of Environmental Economics and Management, Elsevier, vol. 43(2), pages 267-279, March.
    5. Nguimkeu, Pierre & Denteh, Augustine & Tchernis, Rusty, 2019. "On the estimation of treatment effects with endogenous misreporting," Journal of Econometrics, Elsevier, vol. 208(2), pages 487-506.
    6. Kalouptsidis, N. & Psaraki, V., 2010. "Approximations of choice probabilities in mixed logit models," European Journal of Operational Research, Elsevier, vol. 200(2), pages 529-535, January.
    7. repec:ebl:ecbull:v:3:y:2008:i:42:p:1-13 is not listed on IDEAS
    8. Yang Lu, 2019. "Flexible (panel) regression models for bivariate count–continuous data with an insurance application," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1503-1521, October.
    9. Fosgerau, Mogens & Bierlaire, Michel, 2007. "A practical test for the choice of mixing distribution in discrete choice models," Transportation Research Part B: Methodological, Elsevier, vol. 41(7), pages 784-794, August.
    10. Čížek, Pavel, 2008. "General Trimmed Estimation: Robust Approach To Nonlinear And Limited Dependent Variable Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1500-1529, December.
    11. Brenda Castillo-Brais & Ángel León & Juan Mora, 2022. "Estimating Value-at-Risk and Expected Shortfall: Do Polynomial Expansions Outperform Parametric Densities?," Mathematics, MDPI, vol. 10(22), pages 1-17, November.
    12. Jurczenko, Emmanuel & Maillet, Bertrand & Negrea, Bogdan, 2002. "Revisited multi-moment approximate option pricing models: a general comparison (Part 1)," LSE Research Online Documents on Economics 24950, London School of Economics and Political Science, LSE Library.
    13. Duncan J. Mayer & Robert L. Fischer, 2022. "Can a measurement error perspective improve estimation in neighborhood effects research? A hierarchical Bayesian methodology," Social Science Quarterly, Southwestern Social Science Association, vol. 103(5), pages 1260-1272, September.
    14. Paola Zerilli, 2005. "Option pricing and spikes in volatility: theoretical and empirical analysis," Money Macro and Finance (MMF) Research Group Conference 2005 76, Money Macro and Finance Research Group.
    15. Takalo, Tuomas & Tanayama, Tanja & Toivanen, Otto, 2008. "Evaluating innovation policy: a structural treatment effect model of R&D subsidies," Bank of Finland Research Discussion Papers 7/2008, Bank of Finland.
    16. Gallant, A. Ronald, 2023. "Variance–covariance from a metropolis chain on a curved, singular manifold," Journal of Econometrics, Elsevier, vol. 235(2), pages 843-861.
    17. Manuel Arellano & Stéphane Bonhomme, 2023. "Recovering Latent Variables by Matching," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(541), pages 693-706, January.
    18. van der Klaauw, Bas & Koning, Ruud H, 2003. "Testing the Normality Assumption in the Sample Selection Model with an Application to Travel Demand," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(1), pages 31-42, January.
    19. Mittelhammer, Ron C. & Judge, George, 2011. "A family of empirical likelihood functions and estimators for the binary response model," Journal of Econometrics, Elsevier, vol. 164(2), pages 207-217, October.
    20. Jose Luis Moraga-Gonzalez & Zsolt Sandor & Matthijs R. Wildenbeest, 2008. "Nonparametric Estimation of the Costs of Non-Sequential Search," Tinbergen Institute Discussion Papers 07-102/1, Tinbergen Institute.
    21. Che, Yi, 2009. "Mismatch: land reallocations, recovery land rental and land rental market development in rural China," MPRA Paper 39794, University Library of Munich, Germany.

    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:69:y:2013:i:1:p:80-90. 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.