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pp 1–23 | Cite as

Influence diagnostics in mixed effects logistic regression models

  • Alejandra Tapia
  • Victor Leiva
  • Maria del Pilar Diaz
  • Viviana Giampaoli
Original Paper

Abstract

Correlated binary responses are commonly described by mixed effects logistic regression models. This article derives a diagnostic methodology based on the Q-displacement function to investigate local influence of the responses in the maximum likelihood estimates of the parameters and in the predictive performance of the mixed effects logistic regression model. An appropriate perturbation strategy of the probability of success is established, as a form of assessing the perturbation in the response. The diagnostic methodology is evaluated with Monte Carlo simulations. Illustrations with two real-world data sets (balanced and unbalanced) are conducted to show the potential of the proposed methodology.

Keywords

Approximation of integrals Correlated binary responses Metropolis–Hastings and Monte Carlo methods Probability of success R software 

Mathematics Subject Classification

62J20 62J12 

Notes

Acknowledgements

The authors thank the Editors and two referees for their constructive comments on an earlier version of this manuscript which resulted in this improved version. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - Brasil (CAPES) - Finance Code 001, by HPC resources provided by the Information Technology Superintendence of the University of São Paulo, and also by CNPq from Brazil; as well as by the Chilean Council for Scientific and Technology Research (CONICYT) through fellowship “Becas-Chile” (A. Tapia) and FONDECYT 1160868 Grant (V. Leiva) from the Chilean government.

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Copyright information

© Sociedad de Estadística e Investigación Operativa 2018

Authors and Affiliations

  1. 1.Institute of Statistics, Faculty of Economic and Administration SciencesUniversidad Austral de ChileValdiviaChile
  2. 2.School of Industrial EngineeringPontificia Universidad Católica de ValparaísoValparaísoChile
  3. 3.School of Nutrition, Faculty of Medical Sciences and INICSA-CONICETUniversidad Nacional de CórdobaCórdobaArgentina
  4. 4.Institute of Mathematics and StatisticsUniversidade de São PauloSão PauloBrazil

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