The illusion of the illusion of sparsity: an exercise in prior sensitivity

DOI10.1214/21-BJPS503OpenAlexW3091443386MaRDI QIDQ2077428

Bruno Fava, Hedibert Freitas Lopes

Publication date: 21 February 2022

Published in: Brazilian Journal of Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2009.14296

zbMATH Keywords

model selectionshrinkagesparsityhigh dimensional dataBayesian econometrics

Mathematics Subject Classification ID

Statistics (62-XX) Biology and other natural sciences (92-XX)

Related Items (2)

Structural inference in sparse high-dimensional vector autoregressions ⋮ Factor Extraction in Dynamic Factor Models: Kalman Filter Versus Principal Components

Uses Software

• GitHub

Cites Work

• Spike and slab variable selection: frequentist and Bayesian strategies
• \(\ell_1\)-penalized quantile regression in high-dimensional sparse models
• Inference with normal-gamma prior distributions in regression problems
• Sparse Models and Methods for Optimal Instruments With an Application to Eminent Domain
• Efficient Sampling for Gaussian Linear Regression With Arbitrary Priors
• The Bayesian Lasso
• Bayesian Variable Selection in Linear Regression
• Forecasting Using Principal Components From a Large Number of Predictors
• Inference on Treatment Effects after Selection among High-Dimensional Controls
• The Spike-and-Slab LASSO
• Dirichlet–Laplace Priors for Optimal Shrinkage
• Ridge Regression: Biased Estimation for Nonorthogonal Problems
• Economic Predictions With Big Data: The Illusion of Sparsity

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