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Using Bayesian variable selection to analyze regular resolution IV two-level fractional factorial designs

Journal Article · · Quality and Reliability Engineering International
DOI:https://doi.org/10.1002/qre.2022· OSTI ID:1291219
 [1];  [2]
  1. Acadia Univ., Wolfville, NS (Canada)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
+ Show Author Affiliations
Regular two-level fractional factorial designs have complete aliasing in which the associated columns of multiple effects are identical. Here, we show how Bayesian variable selection can be used to analyze experiments that use such designs. In addition to sparsity and hierarchy, Bayesian variable selection naturally incorporates heredity . This prior information is used to identify the most likely combinations of active terms. We also demonstrate the method on simulated and real experiments.
Research Organization:
Los Alamos National Laboratory (LANL)
Sponsoring Organization:
USDOE
Grant/Contract Number:
AC52-06NA25396
OSTI ID:
1291219
Report Number(s):
LA-UR-15-26601
Journal Information:
Quality and Reliability Engineering International, Journal Name: Quality and Reliability Engineering International; ISSN 0748-8017
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English

References (7)

Fast Model Search for Designed Experiments with Complex Aliasing book January 1998
Analysis of Designed Experiments with Complex Aliasing journal July 1992
An Analysis for Unreplicated Fractional Factorials journal February 1986
A Bayesian Variable-Selection Approach for Analyzing Designed Experiments With Complex Aliasing journal November 1997
A Bayesian Analysis of Unreplicated Two-Level Factorials Using Effects Sparsity, Hierarchy, and Heredity journal March 2011
Incorporating Prior Information in Optimal Design for Model Selection journal May 2007
Bayesian variable selection with related predictors journal March 1996

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Related Subjects

42 ENGINEERING
Bayesian
aliasing
fractional factorial
heredity
hierarchy
sparsity