Robust estimation in generalized linear mixed models

95 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Bayesian Inference and Application of Robust Growth Curve Models Using Student's t Distribution (2013, methodology): “For example, Gill (2000) and Yau and Kuk (2002) developed robust methods for linear and generalized linear mixed-effects models (see also Lachos, Dey, & Cancho, 2009; Pinheiro, Liu, & Wu, 2001; Song, Zhang, & Qu, 2007).”
High-Breakdown Inference for Mixed Linear Models (2006, methodology): “More generally, robust estimators have also been proposed for generalized mixed linear models (see Yau and Kuk 2002; Sinha 2004).”
Robust Estimation in Generalized Partial Linear Models for Clustered Data (2005, methodology): “Stefanski, Carroll, and Ruppert (1986), Künsch, Stefanski, and Carroll (1989), and Bianco and Yohai (1996) studied robust estimators for GLMs. Yau and Kuk (2002) and Sinha (2004) considered robust estimation in generalized linear mixed models.”
Maximization by Parts in Likelihood Inference (2005, methodology): “Our algorithm has some similarity to the method of Jiang and Zhang (2001).”
Zero‐Inflated Negative Binomial Mixed Regression Modeling of Over‐Dispersed Count Data with Extra Zeros (2003, methodology): “The GLMM formulation is applicable to a broad class of problems in which the distribution of the response variable conditional on the random components can be expressed in terms of the linear predictors (Yau and Kuk, 2002).”