We introduce a new nonparametric regression estimator that uses prior information on regression shape in the form of a parametric model. In eeect, we nonparametrically encompass the parametric model. We obtain estimates of the regression function and its derivatives along with local parameter estimates that can be interpreted from within the parametric model. We establish the uniform consistenc...

We introduce a new nonparametric regression estimator that uses prior information on regression shape in the form of a parametric model. In e!ect, we nonparametrically encompass the parametric model. We obtain estimates of the regression function and its derivatives along with local parameter estimates that can be interpreted from within the parametric model. We establish the uniform consistenc...

Spatial generalized linear mixed models (SGLMMs) are popular models for spatial data with a non-Gaussian response. Binomial SGLMMs with logit or probit link functions are often used to model spatially dependent binomial random variables. It is known that for independent binomial data, the robit regression model provides a more robust (against extreme observations) alternative to the more popula...

The main feature of the CoIL challenge data is that the observed response is discrete: we only observe whether or not the customers buy caravan insurance. Models for such data were developed in the 1940s and 1950s in the Bio-statistics literature (see references in Cox and Snell, 1989), in particular LOGIT and PROBIT. In the 1970s such techniques became popular in Economics, where a utility max...

DCM (Discrete Choice Models) is a package, written in Ox, for estimating a class of discrete choice models. DCM represents an important development for both the OxMetric and, more generally, microeconometric computing environment in making available a broad range of discrete choice models, including standard binary response models, with notable extensions including conditional mixed logit, mixe...