Corpus ID: 236087632

Decoupling Shrinkage and Selection for the Bayesian Quantile Regression

@inproceedings{Kohns2021DecouplingSA,
  title={Decoupling Shrinkage and Selection for the Bayesian Quantile Regression},
  author={David Kohns and T. Szendrei},
  year={2021}
}
This paper extends the idea of decoupling shrinkage and sparsity for continuous priors to Bayesian Quantile Regression (BQR). The procedure follows two steps: In the first step, we shrink the quantile regression posterior through state of the art continuous priors and in the second step, we sparsify the posterior through an efficient variant of the adaptive lasso, the signal adaptive variable selection (SAVS) algorithm. We propose a new variant of the SAVS which automates the choice of… Expand

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