Influence diagnostics in mixed effects logistic regression models

Alejandra Tapia, Victor Leiva, Maria del Pilar Diaz, Viviana Giampaoli

Research output: Contribution to journalArticlepeer-review

10 Scopus citations

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.

Original languageEnglish
Pages (from-to)920-942
Number of pages23
JournalTest
Volume28
Issue number3
DOIs
StatePublished - 1 Sep 2019
Externally publishedYes

Keywords

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

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