Uncertainty Quantification for the Horseshoe (with Discussion)

By Stéphanie van der Pas, Botond Szab'o, A. van der Vaart. 2016

80 citations recorded by the source corpus.

We begin by introducing the main ideas of the paper under discussion. We discuss some interesting issues regarding adaptive component-wise credible intervals. We then briefly touch upon the concepts of self-similarity and excessive bias restriction. This is then followed by some comments on the extensive simulation study carried out in the paper.

Subfields: Control and Systems Engineering, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a331c0f022010f26219baea7ec1a6588d85b8e50

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Bruce A. Craig
2. Anindya Bhadra
3. Howard D. Bondell
4. Brian J. Reich

In Statistics and Probability

1. B. Naughton
2. Y. Zhang
3. Bruce A. Craig
4. Anindya Bhadra
5. Howard D. Bondell
6. Brian J. Reich
7. Liangjun Su
8. Robert D. Nowak

Citations in the corpus

• Bayesian Regression Using a Prior on the Model Fit: The R2-D2 Shrinkage Prior (2016, background): “Zellner and Siow (1980) recommended these Cauchy priors for model selection problems.”
• The Graphical Horseshoe Estimator for Inverse Covariance Matrices (2017, methodology): “Thus, combining the matrix partition and variable change for Bayesian graphical lasso proposed by Wang (2012) and the variable augmentation for the half-Cauchy prior proposed by Makalic and Schmidt (2016), the graphical horseshoe model has all conditionals in closed form and hence, admits a full Gib”