Parameter Estimation of Generalized Linear Models Without Assuming Their Link Function

Abstract

Canonical generalized linear models (GLM) are completely specified by a finite dimensional vector and a monotonically increasing function called the link function. Standard parameter estimation techniques hold the link function fixed and optimizes over the parameter vector. We propose a parameter-recovery facilitating, jointly-convex, regularized loss functional that is optimized globally over the vector as well as the link function, with best rates possible under a first order oracle model. This widens the scope of GLMs to cases where the link function is unknown.

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Text

Acharyya and Ghosh. "Parameter Estimation of Generalized Linear Models Without Assuming Their Link Function." International Conference on Artificial Intelligence and Statistics, 2015.

Markdown

[Acharyya and Ghosh. "Parameter Estimation of Generalized Linear Models Without Assuming Their Link Function." International Conference on Artificial Intelligence and Statistics, 2015.](https://mlanthology.org/aistats/2015/acharyya2015aistats-parameter/)

BibTeX

@inproceedings{acharyya2015aistats-parameter,
  title     = {{Parameter Estimation of Generalized Linear Models Without Assuming Their Link Function}},
  author    = {Acharyya, Sreangsu and Ghosh, Joydeep},
  booktitle = {International Conference on Artificial Intelligence and Statistics},
  year      = {2015},
  url       = {https://mlanthology.org/aistats/2015/acharyya2015aistats-parameter/}
}