Robust Analysis of Generalized Linear Mixed Models

By Sanjoy K. Sinha. 2004

103 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/14de7c62734aa58993a5b7fb6f53b33ee447fc4e

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In Statistics and Probability

Citations in the corpus

Robust Estimation in Generalized Partial Linear Models for Clustered Data (2005, background): “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.”
Robust estimation in generalized semiparametric mixed models for longitudinal data (2007, methodology): “We approximate the non-parametric function in the GPLMM by a regression spline, and make use of bounded scores and leverage-based weights in the estimating equation to achieve robustness against outliers and influential data points, respectively.”
Bootstrap tests for variance components in generalized linear mixed models (2009, methodology): “Sinha (2004) reanalyzed the data using a binary logistic mixed model. Similarly to Sinha (2004), here we consider a mixed effect logistic model for the conditional mean response μij = E(yij|ui): logit(μij) = β0 + β1 femaleij + β2 ageij + β3 dayaccij + β4 severeij + β5 toiletij + ui,”
High-Breakdown Inference for Mixed Linear Models (2006, background): “More generally, robust estimators have also been proposed for generalized mixed linear models (see Yau and Kuk 2002; Sinha 2004).”
Robust small area estimation (2009, background): “When x’s are continuous, a common choice of theweight functionw(x) is a function of the Mahalanobis distance (x − mx)tS−1 x (x − mx), for some robust estimatesmx and Sx of the location and scale of x, respectively (Sinha, 2004).”