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Fast and Accurate Binary Response Mixed Model Analysis Via Expectation Propagation.
Hall, P; Johnstone, I M; Ormerod, J T; Wand, M P; Yu, J C F.
Afiliação
  • Hall P; School of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
  • Johnstone IM; Department of Statistics, Stanford University, Stanford, CA.
  • Ormerod JT; School of Mathematics and Statistics, University of Sydney, Sydney, Australia.
  • Wand MP; School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, Australia.
  • Yu JCF; School of Mathematical and Physical Sciences, University of Technology Sydney, Ultimo, Australia.
J Am Stat Assoc ; 115(532): 1902-1916, 2020.
Article em En | MEDLINE | ID: mdl-35974897
Expectation propagation is a general prescription for approximation of integrals in statistical inference problems. Its literature is mainly concerned with Bayesian inference scenarios. However, expectation propagation can also be used to approximate integrals arising in frequentist statistical inference. We focus on likelihood-based inference for binary response mixed models and show that fast and accurate quadrature-free inference can be realized for the probit link case with multivariate random effects and higher levels of nesting. The approach is supported by asymptotic calculations in which expectation propagation is seen to provide consistent estimation of the exact likelihood surface. Numerical studies reveal the availability of fast, highly accurate and scalable methodology for binary mixed model analysis. Supplementary materials for this article are available online.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Am Stat Assoc Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Austrália

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: J Am Stat Assoc Ano de publicação: 2020 Tipo de documento: Article País de afiliação: Austrália