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Title: Examining robustness of model selection with half-normal and LASSO plots for unreplicated factorial designs

Abstract

When there are constraints on resources, an unreplicated factorial or fractional factorial design can allow efficient exploration of numerous factor and interaction effects. A half-normal plot is a common graphical tool used to compare the relative magnitude of effects and to identify important effects from these experiments when no estimate of error from the experiment is available. An alternative is to use a least absolute shrinkage and selection operation plot to examine the pattern of model selection terms from an experiment. We examine how both the half-normal and least absolute shrinkage and selection operation plots are impacted by the absence of individual observations or an outlier, and the robustness of conclusions obtained from these 2 techniques for identifying important effects from factorial experiments. As a result, the methods are illustrated with 2 examples from the literature.

Authors:
 [1]; ORCiD logo [2]
  1. Pukyong National Univ., Busan (South Korea)
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOD; USDOE
OSTI Identifier:
1375170
Report Number(s):
LA-UR-16-29402
Journal ID: ISSN 0748-8017
Grant/Contract Number:  
AC52-06NA25396
Resource Type:
Accepted Manuscript
Journal Name:
Quality and Reliability Engineering International
Additional Journal Information:
Journal Volume: 33; Journal Issue: 8; Journal ID: ISSN 0748-8017
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Mathematics; half-normal plot; important effects; LASSO influence plot; LASSO plot; least absolute shrinkage and selection operation

Citation Formats

Jang, Dae -Heung, and Anderson-Cook, Christine Michaela. Examining robustness of model selection with half-normal and LASSO plots for unreplicated factorial designs. United States: N. p., 2017. Web. doi:10.1002/qre.2156.
Jang, Dae -Heung, & Anderson-Cook, Christine Michaela. Examining robustness of model selection with half-normal and LASSO plots for unreplicated factorial designs. United States. https://doi.org/10.1002/qre.2156
Jang, Dae -Heung, and Anderson-Cook, Christine Michaela. Wed . "Examining robustness of model selection with half-normal and LASSO plots for unreplicated factorial designs". United States. https://doi.org/10.1002/qre.2156. https://www.osti.gov/servlets/purl/1375170.
@article{osti_1375170,
title = {Examining robustness of model selection with half-normal and LASSO plots for unreplicated factorial designs},
author = {Jang, Dae -Heung and Anderson-Cook, Christine Michaela},
abstractNote = {When there are constraints on resources, an unreplicated factorial or fractional factorial design can allow efficient exploration of numerous factor and interaction effects. A half-normal plot is a common graphical tool used to compare the relative magnitude of effects and to identify important effects from these experiments when no estimate of error from the experiment is available. An alternative is to use a least absolute shrinkage and selection operation plot to examine the pattern of model selection terms from an experiment. We examine how both the half-normal and least absolute shrinkage and selection operation plots are impacted by the absence of individual observations or an outlier, and the robustness of conclusions obtained from these 2 techniques for identifying important effects from factorial experiments. As a result, the methods are illustrated with 2 examples from the literature.},
doi = {10.1002/qre.2156},
journal = {Quality and Reliability Engineering International},
number = 8,
volume = 33,
place = {United States},
year = {Wed Apr 12 00:00:00 EDT 2017},
month = {Wed Apr 12 00:00:00 EDT 2017}
}

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Works referenced in this record:

SAS Macros for Analysis of Unreplicated 2 k and 2 k - p Designs with a Possible Outlier
journal, January 2008


Finding Bad Values in Factorial Designs—Revisited
journal, June 2006


21st century screening experiments: What, why, and how
journal, January 2016


George'S Column
journal, January 1990