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Efficient hybrid EM for linear and nonlinear mixed effects models with censored response

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  • Vaida, Florin
  • Fitzgerald, Anthony P.
  • DeGruttola, Victor

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  • Vaida, Florin & Fitzgerald, Anthony P. & DeGruttola, Victor, 2007. "Efficient hybrid EM for linear and nonlinear mixed effects models with censored response," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5718-5730, August.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:12:p:5718-5730
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    References listed on IDEAS

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    1. James P. Hughes, 1999. "Mixed Effects Models with Censored Data with Application to HIV RNA Levels," Biometrics, The International Biometric Society, vol. 55(2), pages 625-629, June.
    2. J. G. Booth & J. P. Hobert, 1999. "Maximizing generalized linear mixed model likelihoods with an automated Monte Carlo EM algorithm," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 265-285.
    3. Hulin Wu & A. Adam Ding, 1999. "Population HIV-1 Dynamics In Vivo: Applicable Models and Inferential Tools for Virological Data from AIDS Clinical Trials," Biometrics, The International Biometric Society, vol. 55(2), pages 410-418, June.
    4. Wu L., 2002. "A Joint Model for Nonlinear Mixed-Effects Models With Censoring and Covariates Measured With Error, With Application to AIDS Studies," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 955-964, December.
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    Cited by:

    1. Wan-Lun Wang & Luis M. Castro & Wan-Chen Hsieh & Tsung-I Lin, 2021. "Mixtures of factor analyzers with covariates for modeling multiply censored dependent variables," Statistical Papers, Springer, vol. 62(5), pages 2119-2145, October.
    2. Matos, Larissa A. & Lachos, Victor H. & Balakrishnan, N. & Labra, Filidor V., 2013. "Influence diagnostics in linear and nonlinear mixed-effects models with censored data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 450-464.
    3. Larissa A. Matos & Víctor H. Lachos & Tsung-I Lin & Luis M. Castro, 2019. "Heavy-tailed longitudinal regression models for censored data: a robust parametric approach," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(3), pages 844-878, September.
    4. Yuzhu Tian & Er’qian Li & Maozai Tian, 2016. "Bayesian joint quantile regression for mixed effects models with censoring and errors in covariates," Computational Statistics, Springer, vol. 31(3), pages 1031-1057, September.
    5. Francisco H. C. Alencar & Larissa A Matos & Víctor H. Lachos, 2022. "Finite Mixture of Censored Linear Mixed Models for Irregularly Observed Longitudinal Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 463-486, November.
    6. Victor H. Lachos & Dipankar Bandyopadhyay & Dipak K. Dey, 2011. "Linear and Nonlinear Mixed-Effects Models for Censored HIV Viral Loads Using Normal/Independent Distributions," Biometrics, The International Biometric Society, vol. 67(4), pages 1594-1604, December.
    7. Larissa A. Matos & Luis M. Castro & Víctor H. Lachos, 2016. "Censored mixed-effects models for irregularly observed repeated measures with applications to HIV viral loads," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(4), pages 627-653, December.
    8. Yuzhu Tian & Manlai Tang & Maozai Tian, 2018. "Joint modeling for mixed-effects quantile regression of longitudinal data with detection limits and covariates measured with error, with application to AIDS studies," Computational Statistics, Springer, vol. 33(4), pages 1563-1587, December.

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