Reliable Estimation of Generalized Linear Mixed Models using Adaptive Quadrature

By Sophia Rabe-Hesketh, A. Skrondal, Andrew Pickles. 2002

672 citations recorded by the source corpus.

Subfields: Applied Mathematics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/0f407a945f7c4bfa2f7b0639da194ec8ea435318

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In Applied Mathematics

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Citations in the corpus

What More Than Parental Income, Education and Occupation? An Exploration of What Swedish Siblings Get from Their Parents (2010, background): “Mother’s education has only a small, gender-neutral effect on the size of the sibling correlations.29 Least Squares algorithm (see Rabe-Hesketh et al. 2002).”
Correcting for covariate measurement error in logistic regression using nonparametric maximum likelihood estimation (2003, background): “Adaptive quadrature (Naylor and Smith, 1982) can overcome these problems and has been implemented in gllamm (Rabe-Hesketh et al., 2002, 2003b).”
An EM algorithm for the estimation of parametric and nonparametric hierarchical nonlinear models (2004, background): “Lesaffre and Spiessens (2001) and Rabe-Hesketh, Skrondal, and Pickles (2002) showed that the number of quadrature points needs to be very large in some situations.”
Estimation of Multinomial Logit Models with Unobserved Heterogeneity using Maximum Simulated Likelihood (2006, methodology): “Gauss Hermite and adaptive quadrature Gauss Hermite and adaptive quadrature is discussed in detail in Rabe-Hesketh, Skrondal, and Pickles (2002).”
Open, Semi-Open and Closed Innovators: Towards an Explanation of Degree of Openness (2010, methodology): “This method is discussed and compared with other methods in Rabe-Hesketh et al. (2002), who developed a program to run it with Stata.”
Generalized multilevel structural equation modeling (2004, methodology): “For details of our implementation of adaptive quadrature, see Rabe-Hesketh, Skrondal, and Pickles (2002, 2004).”
Maximum Likelihood Estimation of Generalized Linear Models with Covariate Measurement Error (2003, positive): “This ‘wrapper’ command uses the reliable adaptive quadrature method (Rabe-Hesketh et al. 2002) implemented in the general gllamm command but is easier to use than gllamm because of its tailor-made syntax and output.”
High-dimensional maximum marginal likelihood item factor analysis by adaptive quadrature (2005, background): “Similar considerations apply in other multilevel treatments of binary and polytomous data (see Hedeker and Gibbons, 1994), a fact confirmed by a number of recent papers on generalized linear mixed models (Lesaffre and Spiessens, 2001; Rabe-Hesketh, Skrondal, and Pickles 2002, 2005b ).”