Spikeslab: Prediction and variable selection using spike and slab regression

Research output: Contribution to journalArticle

13 Citations (Scopus)

Abstract

Weighted generalized ridge regression offers unique advantages in correlated highdimensional problems. Such estimators can be efficiently computed using Bayesian spike and slab models and are effective for prediction. For sparse variable selection, a generalization of the elastic net can be used in tandem with these Bayesian estimates. In this article, we describe the R-software package spikeslab for implementing this new spike and slabprediction and variable selection methodology.

Original languageEnglish
Pages (from-to)68-73
Number of pages6
JournalR Journal
Volume2
Issue number2
StatePublished - Dec 1 2010

Fingerprint

Variable Selection
Spike
Software packages
Regression
Elastic Net
Ridge Regression
Prediction
Software Package
Estimator
Methodology
Estimate
Variable selection
Model
Generalization
Ridge regression
Software

ASJC Scopus subject areas

  • Statistics and Probability
  • Numerical Analysis
  • Statistics, Probability and Uncertainty

Cite this

Spikeslab : Prediction and variable selection using spike and slab regression. / Ishwaran, Hemant; Kogalur, Udaya B.; Rao, Jonnagadda S.

In: R Journal, Vol. 2, No. 2, 01.12.2010, p. 68-73.

Research output: Contribution to journalArticle

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