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Quasi-Monte Carlo estimation in generalized linear mixed models

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  • Pan, Jianxin
  • Thompson, Robin

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  • Pan, Jianxin & Thompson, Robin, 2007. "Quasi-Monte Carlo estimation in generalized linear mixed models," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5765-5775, August.
    • Handle: RePEc:eee:csdana:v:51:y:2007:i:12:p:5765-5775
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    References listed on IDEAS

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    1. 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.
    2. Youngjo Lee & John A. Nelder, 2006. "Double hierarchical generalized linear models (with discussion)," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 55(2), pages 139-185, April.
    3. Jianxin Pan & Robin Thompson, 2003. "Gauss-Hermite Quadrature Approximation for Estimation in Generalised Linear Mixed Models," Computational Statistics, Springer, vol. 18(1), pages 57-78, March.
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    1. Ueckert, Sebastian & Mentré, France, 2017. "A new method for evaluation of the Fisher information matrix for discrete mixed effect models using Monte Carlo sampling and adaptive Gaussian quadrature," Computational Statistics & Data Analysis, Elsevier, vol. 111(C), pages 203-219.
    2. Reza Drikvandi & Geert Verbeke & Geert Molenberghs, 2017. "Diagnosing misspecification of the random-effects distribution in mixed models," Biometrics, The International Biometric Society, vol. 73(1), pages 63-71, March.
    3. Philipson, Pete & Hickey, Graeme L. & Crowther, Michael J. & Kolamunnage-Dona, Ruwanthi, 2020. "Faster Monte Carlo estimation of joint models for time-to-event and multivariate longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 151(C).
    4. F. Y. Kuo & W. T. M. Dunsmuir & I. H. Sloan & M. P. Wand & R. S. Womersley, 2008. "Quasi-Monte Carlo for Highly Structured Generalised Response Models," Methodology and Computing in Applied Probability, Springer, vol. 10(2), pages 239-275, June.
    5. Laura Zieger & John Jerrim & Jake Anders & Nikki Shure, 2020. "Conditioning: How background variables can influence PISA scores," CEPEO Working Paper Series 20-09, UCL Centre for Education Policy and Equalising Opportunities, revised Apr 2020.
    6. Keunbaik Lee & Hoimin Jung & Jae Keun Yoo, 2019. "Modeling of the ARMA random effects covariance matrix in logistic random effects models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(2), pages 281-299, June.

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