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Flexible Parametric Measurement Error Models

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  • Raymond J. Carroll
  • Kathryn Roeder
  • Larry Wasserman

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  • Raymond J. Carroll & Kathryn Roeder & Larry Wasserman, 1999. "Flexible Parametric Measurement Error Models," Biometrics, The International Biometric Society, vol. 55(1), pages 44-54, March.
    • Handle: RePEc:bla:biomet:v:55:y:1999:i:1:p:44-54
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    File URL: http://hdl.handle.net/10.1111/j.0006-341X.1999.00044.x
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    References listed on IDEAS

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    1. Sylvia. Richardson & Peter J. Green, 1997. "On Bayesian Analysis of Mixtures with an Unknown Number of Components (with discussion)," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(4), pages 731-792.
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    Cited by:

    1. Bertrand, Aurelie & Van Keilegom, Ingrid & Legrand, Catherine, 2017. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," LIDAM Discussion Papers ISBA 2017025, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. 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.
    3. Kim Yeaji & Antenangeli Leonardo & Kirkland Justin, 2016. "Measurement Error and Attenuation Bias in Exponential Random Graph Models," Statistics, Politics and Policy, De Gruyter, vol. 7(1-2), pages 29-54, December.
    4. Arana, Jorge E. & Leon, Carmelo J., 2005. "Flexible mixture distribution modeling of dichotomous choice contingent valuation with heterogenity," Journal of Environmental Economics and Management, Elsevier, vol. 50(1), pages 170-188, July.
    5. John Staudenmayer & Donna Spiegelman, 2002. "Segmented Regression in the Presence of Covariate Measurement Error in Main Study/Validation Study Designs," Biometrics, The International Biometric Society, vol. 58(4), pages 871-877, December.
    6. Abhra Sarkar & Bani K. Mallick & Raymond J. Carroll, 2014. "Bayesian semiparametric regression in the presence of conditionally heteroscedastic measurement and regression errors," Biometrics, The International Biometric Society, vol. 70(4), pages 823-834, December.
    7. Aurélie Bertrand & Ingrid Van Keilegom & Catherine Legrand, 2019. "Flexible parametric approach to classical measurement error variance estimation without auxiliary data," Biometrics, The International Biometric Society, vol. 75(1), pages 297-307, March.
    8. Bani Mallick & F. Owen Hoffman & Raymond J. Carroll, 2002. "Semiparametric Regression Modeling with Mixtures of Berkson and Classical Error, with Application to Fallout from the Nevada Test Site," Biometrics, The International Biometric Society, vol. 58(1), pages 13-20, March.
    9. Domingo Benítez & Gustavo Montero & Eduardo Rodríguez & David Greiner & Albert Oliver & Luis González & Rafael Montenegro, 2020. "A Phenomenological Epidemic Model Based On the Spatio-Temporal Evolution of a Gaussian Probability Density Function," Mathematics, MDPI, vol. 8(11), pages 1-22, November.
    10. A. Guolo, 2008. "A Flexible Approach to Measurement Error Correction in Case–Control Studies," Biometrics, The International Biometric Society, vol. 64(4), pages 1207-1214, December.
    11. Daniel W. Schafer, 2001. "Semiparametric Maximum Likelihood for Measurement Error Model Regression," Biometrics, The International Biometric Society, vol. 57(1), pages 53-61, March.
    12. Duchwan Ryu & Erning Li & Bani K. Mallick, 2011. "Bayesian Nonparametric Regression Analysis of Data with Random Effects Covariates from Longitudinal Measurements," Biometrics, The International Biometric Society, vol. 67(2), pages 454-466, June.
    13. Liang Li & Jun Shao & Mari Palta, 2005. "A Longitudinal Measurement Error Model with a Semicontinuous Covariate," Biometrics, The International Biometric Society, vol. 61(3), pages 824-830, September.
    14. Xiaoqiong Fang & Andy W. Chen & Derek S. Young, 2023. "Predictors with measurement error in mixtures of polynomial regressions," Computational Statistics, Springer, vol. 38(1), pages 373-401, March.
    15. Elaheh Torkashvand & Mohammad Jafari Jozani & Mahmoud Torabi, 2016. "Constrained Bayes estimation in small area models with functional measurement error," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(4), pages 710-730, December.
    16. Pingping Qu & Yinsheng Qu, 2000. "A Bayesian Approach to Finite Mixture Models in Bioassay via Data Augmentation and Gibbs Sampling and Its Application to Insecticide Resistance," Biometrics, The International Biometric Society, vol. 56(4), pages 1249-1255, December.
    17. 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.
    18. Torabi, Mahmoud, 2013. "Likelihood inference in generalized linear mixed measurement error models," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 549-557.
    19. 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.
    20. Gustafson, Paul & Le, Nhu D. & Vallée, Marc, 2000. "Parametric Bayesian analysis of case-control data with imprecise exposure measurements," Statistics & Probability Letters, Elsevier, vol. 47(4), pages 357-363, May.
    21. Martin L. Hazelton & Berwin A. Turlach, 2010. "Semiparametric Density Deconvolution," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 37(1), pages 91-108, March.
    22. Michal Pešta, 2021. "Changepoint in Error-Prone Relations," Mathematics, MDPI, vol. 9(1), pages 1-25, January.
    23. A. Charisse Farr & Kerrie Mengersen & Fabrizio Ruggeri & Daniel Simpson & Paul Wu & Prasad Yarlagadda, 2020. "Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach," International Statistical Review, International Statistical Institute, vol. 88(2), pages 335-353, August.

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