Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies.

@article{Savitsky2011VariableSF,
  title={Variable Selection for Nonparametric Gaussian Process Priors: Models and Computational Strategies.},
  author={Terrance D. Savitsky and Marina Vannucci and Naijun Sha},
  journal={Statistical science : a review journal of the Institute of Mathematical Statistics},
  year={2011},
  volume={26 1},
  pages={130-149}
}
This paper presents a unified treatment of Gaussian process models that extends to data from the exponential dispersion family and to survival data. Our specific interest is in the analysis of data sets with predictors that have an a priori unknown form of possibly nonlinear associations to the response. The modeling approach we describe incorporates Gaussian processes in a generalized linear model framework to obtain a class of nonparametric regression models where the covariance matrix… CONTINUE READING
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