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Small sample inference for the fixed effects in the mixed linear model

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  • Manor, Orly
  • Zucker, D.M.David M.

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  • Manor, Orly & Zucker, D.M.David M., 2004. "Small sample inference for the fixed effects in the mixed linear model," Computational Statistics & Data Analysis, Elsevier, vol. 46(4), pages 801-817, July.
    • Handle: RePEc:eee:csdana:v:46:y:2004:i:4:p:801-817
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    References listed on IDEAS

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    1. S. J. Welham & R. Thompson, 1997. "Likelihood Ratio Tests for Fixed Model Terms using Residual Maximum Likelihood," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 59(3), pages 701-714.
    2. Verbeke, Geert & Lesaffre, Emmanuel, 1997. "The effect of misspecifying the random-effects distribution in linear mixed models for longitudinal data," Computational Statistics & Data Analysis, Elsevier, vol. 23(4), pages 541-556, February.
    3. Daowen Zhang & Marie Davidian, 2001. "Linear Mixed Models with Flexible Distributions of Random Effects for Longitudinal Data," Biometrics, The International Biometric Society, vol. 57(3), pages 795-802, September.
    4. Kauermann G. & Carroll R.J., 2001. "A Note on the Efficiency of Sandwich Covariance Matrix Estimation," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1387-1396, December.
    5. Shin, Dong Wan & Park, Chul Gyu & Park, Taesung, 2001. "Testing for one-sided group effects in repeated measures study," Computational Statistics & Data Analysis, Elsevier, vol. 37(2), pages 233-247, August.
    6. Xiang, Liming & Tse, Siu-Keung & Lee, Andy H., 2002. "Influence diagnostics for generalized linear mixed models: applications to clustered data," Computational Statistics & Data Analysis, Elsevier, vol. 40(4), pages 759-774, October.
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    Cited by:

    1. Guolo, Annamaria & Brazzale, Alessandra R. & Salvan, Alessandra, 2006. "Improved inference on a scalar fixed effect of interest in nonlinear mixed-effects models," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1602-1613, December.
    2. Elff, Martin & Heisig, Jan Paul & Schaeffer, Merlin & Shikano, Susumu, 2016. "No Need to Turn Bayesian in Multilevel Analysis with Few Clusters: How Frequentist Methods Provide Unbiased Estimates and Accurate Inference," SocArXiv z65s4, Center for Open Science.
    3. Vargas, Tiago M. & Ferrari, Silvia L.P. & Lemonte, Artur J., 2014. "Improved likelihood inference in generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 74(C), pages 110-124.
    4. McNabb, Carolyn Beth & Murayama, Kou, 2021. "Unnecessary reliance on multilevel modelling to analyse nested data in neuroscience: When a traditional summary-statistics approach suffices," OSF Preprints h4s9f, Center for Open Science.

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