An efficient computational approach for prior sensitivity analysis and cross-validation

Journal article

Prior sensitivity analysis and cross-validation are important tools in Bayesian statistics. However, due to the computational expense of implementing existing methods, these techniques are rarely used. In this paper, the authors show how it is possible to use sequential Monte Carlo methods to create an efficient and automated algorithm to perform these tasks. They apply the algorithm to the computation of regularization path plots and to assess the sensitivity of the tuning parameter in g-prior model selection. They then demonstrate the algorithm in a cross-validation context and use it to select the shrinkage parameter in Bayesian regression. © 2010 Statistical Society of Canada.

Authors: Bornn, L; Doucet, A; Gottardo, R

• Publication status: Published
• Journal: CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE
• Volume: 38
• Issue: 1
• Pages: 47-64
• Publication date: 2010-03-01
• EISSN: 1708-945X
• ISSN: 0319-5724
• Language: English
• Keywords: Markov chain Monte Carlo; prior sensitivity; variable selection; penalized regression; sequential Monte Carlo; g-prior; Cross-validation; regularization path plot