The R Journal: accepted article

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Semiparametric Generalized Linear Models with the gldrm Package PDF download
Michael J. Wurm and Paul J. Rathouz

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 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.

Received: 2017-07-23; online 2018-05-21, supplementary material, (644 bytes)
CRAN packages: gldrm, stats, datasets

CC BY 4.0
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  author = {Michael J. Wurm and Paul J. Rathouz},
  title = {{Semiparametric Generalized Linear Models with the gldrm
  year = {2018},
  journal = {{The R Journal}},
  url = {}