Lasso Meets Horseshoe: A Survey

• Anindya Bhadra ^[1] ; Jyotishka Datta ^[3] ; Polson, Nicholas G. ^[2] ; Brandon Willard ^[2]
  1. [1] Purdue University

     Purdue University

     Township of Wabash, Estados Unidos

     
  2. [2] University of Chicago

     University of Chicago

     City of Chicago, Estados Unidos

     
  3. [3] University of Arkansas

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• Localización: Statistical science, ISSN 0883-4237, Vol. 34, Nº. 3, 2019, págs. 405-427
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • The goal of this paper is to contrast and survey the major advances in two of the most commonly used high-dimensional techniques, namely, the Lasso and horseshoe regularization. Lasso is a gold standard for predictor selection while horseshoe is a state-of-the-art Bayesian estimator for sparse signals. Lasso is fast and scalable and uses convex optimization whilst the horseshoe is nonconvex. Our novel perspective focuses on three aspects: (i) theoretical optimality in high-dimensional inference for the Gaussian sparse model and beyond, (ii) efficiency and scalability of computation and (iii) methodological development and performance.