Statistics, Department of

The R Journal
Date of this Version
7-2018
Document Type
Article
Citation
The R Journal (July 2018) 10(1); Editor: John Verzani
Abstract
This paper introduces a new algorithm to estimate and perform inferences on a recently proposed and developed semiparametric generalized linear model (glm). Rather than selecting a particular parametric exponential family model, such as the Poisson distribution, this semiparametric glm assumes that the response is drawn from the more general exponential tilt family. The regression coefficients and unspecified reference distribution are estimated by maximizing a semiparametric likelihood. The new algorithm incorporates several computational stability and efficiency improvements over the algorithm originally proposed. In particular, the new algorithm performs well for either small or large support for the nonparametric response distribution. The algorithm is implemented in a new Rpackage called gldrm.
Included in
Numerical Analysis and Scientific Computing Commons, Programming Languages and Compilers Commons
Comments
Copyright 2018, The R Foundation. Open access material. License: CC BY 4.0 International