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 like lihood. 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 R package called gldrm.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2018-027.zip
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For attribution, please cite this work as
Wurm & Rathouz, "Semiparametric Generalized Linear Models with the gldrm Package", The R Journal, 2018
BibTeX citation
@article{RJ-2018-027,
author = {Wurm, Michael J. and Rathouz, Paul J.},
title = {Semiparametric Generalized Linear Models with the gldrm Package},
journal = {The R Journal},
year = {2018},
note = {https://doi.org/10.32614/RJ-2018-027},
doi = {10.32614/RJ-2018-027},
volume = {10},
issue = {1},
issn = {2073-4859},
pages = {288-307}
}