Categorical models for local names

Categorical Models for Local Names

This paper describes the construction of categorical models for the nu-calculus, a language that combines higher-order functions with dynamically created names. Names are created with local scope, they can be compared with each other and passed around through function application, but that is all. The intent behind this language is to examine one aspect of the imperative character of Standard M...

Comparison between Local Estimates for Multi-categorical Varying-coeecent Models

Varying coeecient models with discrete values of the eeect modiier may be estimated by maximum likelihood or weighted least square techniques. We compare bias reduction methods for both estimates as well as the performance of the estimates as compared to each other.

Comparison between local estimates for multi-categorical varying-coefficent models

A Local Estimating Approach in Multi-categorical Varying-coeecient Models

Varying-coeecient models are an extension of generalized linear models. In this case the coeecients are allowed to vary smoothly with the value of other variables. We present a "local Fisher scoring backktting" algorithm based on a weighted local likelihood approach where the components are iteratively tted. An example will illustrate the usefulness of this approach.

Bayesian Models for Categorical Data