Influence diagnostics in mixed effects logistic regression models

Alejandra Tapia, Victor Leiva (), Maria del Pilar Diaz and Viviana Giampaoli
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Alejandra Tapia: Universidad Austral de Chile
Victor Leiva: Pontificia Universidad Católica de Valparaíso
Maria del Pilar Diaz: Universidad Nacional de Córdoba
Viviana Giampaoli: Universidade de São Paulo

TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, 2019, vol. 28, issue 3, No 17, 920-942

Abstract: 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; 62J20; 62J12 (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s11749-018-0613-3

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