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• W1976031668 abstract "We review several asymmetrical links for binary regression models and present a unified approach for two skew-probit links proposed in the literature. Moreover, under skew-probit link, conditions for the existence of the ML estimators and the posterior distribution under improper priors are established. The framework proposed here considers two sets of latent variables which are helpful to implement the Bayesian MCMC approach. A simulation study to criteria for models comparison is conducted and two applications are made. Using different Bayesian criteria we show that, for these data sets, the skew-probit links are better than alternative links proposed in the literature." @default.
• W1976031668 created "2016-06-24" @default.
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• W1976031668 date "2010-02-10" @default.
• W1976031668 modified "2023-09-30" @default.
• W1976031668 title "A Framework for Skew-Probit Links in Binary Regression" @default.
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• W1976031668 doi "https://doi.org/10.1080/03610920902783849" @default.
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