Flexible generalized t-link models for binary response data

A critical issue in modelling binary response data is the choiceof the links. We introduce a new link based on the generalizedt-distribution. There are two parameters in the generalizedt-link: one parameter purely controls the heaviness of the tailsof the link and the second parameter controls the scale of thelink. Two major advantages are offered by the generalized t-links.First, a symmetric generalized t-link with an unknown shapeparameter is much more identifiable than a Student t-link withunknown degrees of freedom and a known scale parameter. Secondly,skewed generalized t-links with both unknown shape and scaleparameters provide much more flexible and improved skewed linkregression models than the existing skewed links. Various theoreticalproperties and attractive features of the proposed links areexamined and explored in detail. An efficient Markov chain MonteCarlo algorithm is developed for sampling from the posteriordistribution. The deviance information criterion measure isused for guiding the choice of links. The proposed methodologyis motivated and illustrated by prostate cancer data.