Semiparametric Generalized Linear Models with the gldrm Package

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.

Michael J. Wurm , Paul J. Rathouz

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For attribution, please cite this work as

Wurm & Rathouz, "The R Journal: Semiparametric Generalized Linear Models with the gldrm Package", The R Journal, 2018

BibTeX citation

  author = {Wurm, Michael J. and Rathouz, Paul J.},
  title = {The R Journal: Semiparametric Generalized Linear Models with the gldrm Package},
  journal = {The R Journal},
  year = {2018},
  note = {},
  doi = {10.32614/RJ-2018-027},
  volume = {10},
  issue = {1},
  issn = {2073-4859},
  pages = {288-307}