MAXIMUM LIKELIHOOD INFERENCE IN ROBUST LINEAR MIXED-EFFECTS MODELS USING MULTIVARIATE t DISTRIBUTIONS

By Peter X.-K. Song, Peng Zhang, Annie Qu. 2007

53 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/0023ba578aa57b6a99c6932dc9f6f48fc4d31634

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1. Zhiyong Zhang
2. Xin Tong
3. K. Lai
4. Zhenqiu Lu
5. Haiyan Liu

Citations in the corpus

• Simplex Mixed‐Effects Models for Longitudinal Proportional Data (2008, unknown): “See, for instance, Pinheiro et al. (2001) and Song et al. (2007).”
• Bayesian quantile regression for longitudinal data models (2012, unknown): “These approaches provide a more flexible way to analyse longitudinal data models with any random effects distributions, such as Cauchy and other heavy-tailed distributions that are commonly used in speech and image data 17–19 .”
• Bayesian Inference and Application of Robust Growth Curve Models Using Student's t Distribution (2013, methodology): “Second, The parametric robust methods developed for mix-effects models and latent variable models based on t distributions or contaminated normal distributions have hardly been applied to growth curve analysis directly (e.g., Pinheiro et al., 2001; Song et al., 2007).”