MAP model selection in Gaussian regression

@inproceedings{Abramovich2010MAPMS,
  title={MAP model selection in Gaussian regression},
  author={Felix Abramovich and Vadim Grinshtein},
  year={2010}
}
We consider a Bayesian approach to model selection in Gaussian linear regression, where the number of predictors might be much larger than the number of observations. From a frequentist view, the proposed procedure results in the penalized least squares estimation with a complexity penalty associated with a prior on the model size. We investigate the optimality properties of the resulting model selector. We establish the oracle inequality and specify conditions on the prior that imply its… CONTINUE READING
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