Modelo de regressão para dados binários com mistura de funções de ligação
Eugenio, Nicholas Wagner
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A regression model for binary data with mixture of four link functions (logit, probit, complementary log log and Stukel) is shown and these functions are particular cases of the model. The frequentist estimation procedure is exposed and, by simulation studies, it is notable that, comparing with other models, the link function proposed presents a better performance in proportions’ estimations, while for predctions they are all equal. Its flexibility in being both a symmetric or an assymmetric link function is corroborated on the real data analisys results, as the simulations. Furthermore, it is shown a case where the mixture associates total weight for a link function because it is no possible to improve the results by mixing other functions.