Semiparametric Generalized Linear Models: Bayesian Approaches
نویسنده
چکیده
Generalized linear models are one of the most widely used tools of the data analyst. However, the model assumes that the structure of the regression relationship between the response and the covariates is linear on a known transformed scale. We focus here on diierent methods to perform the same type of analyses. These involve using nonparametric models to determine the relationship between the response and covariates after the usual transformation has been carried out. We demonstrate how such a semiparametric model performs for binary regression.
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تاریخ انتشار 1999