useR! 2024

Binary regression models using logit or probit link functions have been widely employed in examining the relationship between binary responses and covariates. However, misspecification of the link function can result in poor model fit and compromise the significance of covariate effects. In this study, we present a local influence diagnostic method associated with a new family of link functions that allows evaluating the sensitivity of symmetric links towards asymmetric ones. This new family offers a comprehensive model that encompasses nested symmetric cases. Furthermore, we present a local influence diagnostic method to evaluate the sensitivity of odds ratios. Monte Carlo simulations are performed to evaluate both the performance of the diagnostic method and the parameter estimation of the overall model, complemented by illustrations using medical data related to menstruation and respiratory problems. The results confirm the effectiveness of our proposal, highlighting the critical role of statistical diagnostics in modeling.