Bayesian lasso with effect heredity principle

Hidehisa Noguchi, Yoshikazu Ojima, Seiichi Yasui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Bayesian Lasso is a variable selection method that can be applied in situations where there are more variables than observations; thus, both main effects and interaction effects can be considered in screening experiments. To apply the Bayesian framework to experiments involving the effect heredity principle, which governs the relationships between interactions and their corresponding main effects, several initial tunings of the Bayesian framework are required. However, it is rather unnatural to specify these tuning values before running an experiment. In this paper, we propose models that do not require the initial tuning values to be specified in advance. The proposed methods are demonstrated with screening examples such as Plackett-Burman and mixed-level design.

Original languageEnglish
Title of host publicationFrontiers in Statistical Quality Control - 11th International Workshop on Intelligent Statistical Quality Control, 2013
EditorsSven Knoth, Wolfgang Schmid
PublisherKluwer Academic Publishers
Pages355-365
Number of pages11
ISBN (Print)9783319123547
DOIs
Publication statusPublished - 1 Jan 2015
Event11th International Workshop on Intelligent Statistical Quality Control, 2013 - Sydney, Australia
Duration: 20 Aug 201323 Aug 2013

Publication series

NameFrontiers in Statistical Quality Control 10
Volume11

Conference

Conference11th International Workshop on Intelligent Statistical Quality Control, 2013
Country/TerritoryAustralia
CitySydney
Period20/08/1323/08/13

Keywords

  • Factor interaction
  • Hierarchical models
  • Variable selection

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