TY - JOUR
T1 - Influence diagnostics in mixed effects logistic regression models
AU - Tapia, Alejandra
AU - Leiva, Victor
AU - Diaz, Maria del Pilar
AU - Giampaoli, Viviana
N1 - Funding Information:
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 Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior - Brasil (CAPES) - Finance Code 001, by HPC resources provided by the Information Technology Superintendence of the University of S?o 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.
Publisher Copyright:
© 2018, Sociedad de Estadística e Investigación Operativa.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - 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.
AB - 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.
KW - Approximation of integrals
KW - Correlated binary responses
KW - Metropolis–Hastings and Monte Carlo methods
KW - Probability of success
KW - R software
UR - http://www.scopus.com/inward/record.url?scp=85053665891&partnerID=8YFLogxK
U2 - 10.1007/s11749-018-0613-3
DO - 10.1007/s11749-018-0613-3
M3 - Article
AN - SCOPUS:85053665891
VL - 28
SP - 920
EP - 942
JO - Test
JF - Test
SN - 1133-0686
IS - 3
ER -