S
Sophia Rabe-Hesketh
Researcher at University of California, Berkeley
Publications - 205
Citations - 24948
Sophia Rabe-Hesketh is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Generalized linear mixed model & Random effects model. The author has an hindex of 65, co-authored 201 publications receiving 23601 citations. Previous affiliations of Sophia Rabe-Hesketh include University of London & University of California.
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Multilevel and Longitudinal Modeling Using Stata
TL;DR: In this paper, the authors present a linear variance-components model for expiratory flow measurements, which is based on the Mini Wright measurements, and a three-level logistic random-intercept model.
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Meta-Analysis of Regional Brain Volumes in Schizophrenia
Ian C. Wright,Sophia Rabe-Hesketh,Peter W.R. Woodruff,Anthony S. David,Robin M. Murray,Edward T. Bullmore +5 more
TL;DR: In this article, the authors conducted a systematic search for structural magnetic resonance imaging (MRI) studies of patients with schizophrenia that reported volume measurements of selected cortical, subcortical, and ventricular regions in relation to comparison groups.
Book
Generalized Latent Variable Modeling: Multilevel, Longitudinal, and Structural Equation Models
TL;DR: In this paper, a generalized linear model is proposed to generate flexible distributions of latent variables and generate flexible distribution of the latent variables' responses, which can be used to estimate the duration or survival of an individual.
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Global, voxel, and cluster tests, by theory and permutation, for a difference between two groups of structural MR images of the brain
Edward T. Bullmore,John Suckling,S Overmeyer,Sophia Rabe-Hesketh,Eric Taylor,Michael Brammer +5 more
TL;DR: Almost entirely automated procedures for estimation of global, voxel, and cluster-level statistics to test the null hypothesis of zero neuroanatomical difference between two groups of structural magnetic resonance imaging (MRI) data are described.
Journal ArticleDOI
Generalized multilevel structural equation modeling
TL;DR: Maximum likelihood estimation and empirical Bayes latent score prediction within the GLLAMM framework can be performed using adaptive quadrature in gllamm, a freely available program running in Stata.