Abstract: Logit model is the most popular binary regression models for modelling binary response data.When dealing with unbalanced data,Logit model will cause link misspecification.A more flexible model of alpha-stable model,is introduced to fit unbalanced data by setting alpha-stable distribution as the link function.For model estimation,since alpha-stable distribution admits no closed-form expression for the density,we employ expectation propagation with approximate Bayesian computation (EP-ABC) algorithm.It overcomes the difficulties that high dimensionality results in low acceptance rate through data partitioning.According to the simulation results,alpha-stable model performs better than Logit,Probit,Cloglog or GEV model in fitting both balanced and unbalanced data.