Map model selection in gaussian regression

Felix Abramovich, Vadim Grinshtein

Research output: Contribution to journalArticlepeer-review


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 asymptotic minimaxity within a wide range of sparse and dense settings for “nearly-orthogonal” and “multicollinear” designs.

Original languageEnglish
Pages (from-to)932-949
Number of pages18
JournalElectronic Journal of Statistics
StatePublished - 2010


  • Adaptivity
  • Complexity penalty
  • Gaussian linear regression
  • Maximum a posteriori rule
  • Minimax estimation
  • Model selection
  • Oracle inequality
  • Sparsity


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