Parameter Estimation of Generalized Linear Models without Assuming their Link Function

@InProceedings{pmlr-v38-acharyya15,
  title = 	 {{Parameter Estimation of Generalized Linear Models without Assuming their Link Function}},
  author = 	 {Acharyya, Sreangsu and Ghosh, Joydeep},
  booktitle = 	 {Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics},
  pages = 	 {10--18},
  year = 	 {2015},
  editor = 	 {Lebanon, Guy and Vishwanathan, S. V. N.},
  volume = 	 {38},
  series = 	 {Proceedings of Machine Learning Research},
  address = 	 {San Diego, California, USA},
  month = 	 {09--12 May},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v38/acharyya15.pdf},
  url = 	 {https://proceedings.mlr.press/v38/acharyya15.html},
  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.}
}