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Title: Assessing robustness of factor ranking for supersaturated designs

Abstract

Supersaturated designs can potentially be a beneficial tool for efficiently exploring a large number of factors with a moderately sized design. However, because more factors are being considered than there are runs, the stability of the identified factors depends heavily on effect sparsity and the lack of highly influential observations. A helpful tool for the analysis of supersaturated designs is least absolute shrinkage and selection operation (LASSO), which is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. To understand the impact of individual observations on the selected factors, the LASSO influence plot was created. This work describes an application of this plot and its variants that can be used to identify influential points, increase understanding of the impact of individual observations on model parameters, and the robustness of results in analyses with supersaturated designs. These graphical methods can serve as a complement to other regression diagnostics techniques in the LASSO regression setting.

Authors:
 [1]; ORCiD logo [2]
  1. Pukyong National Univ., Busan (Korea). Dept. of Statistics
  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.:
USDOE; USDOD; National Research Foundation of Korea (NRF)
OSTI Identifier:
1467262
Report Number(s):
LA-UR-17-24159
Journal ID: ISSN 0748-8017
Grant/Contract Number:  
AC52-06NA25396; 2017R1D1A3B03028648
Resource Type:
Accepted Manuscript
Journal Name:
Quality and Reliability Engineering International
Additional Journal Information:
Journal Volume: 34; Journal Issue: 3; Journal ID: ISSN 0748-8017
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Supersaturated designs; influential observations; LASSO influence plots; LASSO variable selection ranking plots; 3-D LASSO influence plots

Citation Formats

Jang, Dae-Heung, and Anderson-Cook, Christine M. Assessing robustness of factor ranking for supersaturated designs. United States: N. p., 2018. Web. doi:10.1002/qre.2262.
Jang, Dae-Heung, & Anderson-Cook, Christine M. Assessing robustness of factor ranking for supersaturated designs. United States. doi:10.1002/qre.2262.
Jang, Dae-Heung, and Anderson-Cook, Christine M. Tue . "Assessing robustness of factor ranking for supersaturated designs". United States. doi:10.1002/qre.2262. https://www.osti.gov/servlets/purl/1467262.
@article{osti_1467262,
title = {Assessing robustness of factor ranking for supersaturated designs},
author = {Jang, Dae-Heung and Anderson-Cook, Christine M.},
abstractNote = {Supersaturated designs can potentially be a beneficial tool for efficiently exploring a large number of factors with a moderately sized design. However, because more factors are being considered than there are runs, the stability of the identified factors depends heavily on effect sparsity and the lack of highly influential observations. A helpful tool for the analysis of supersaturated designs is least absolute shrinkage and selection operation (LASSO), which is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. To understand the impact of individual observations on the selected factors, the LASSO influence plot was created. This work describes an application of this plot and its variants that can be used to identify influential points, increase understanding of the impact of individual observations on model parameters, and the robustness of results in analyses with supersaturated designs. These graphical methods can serve as a complement to other regression diagnostics techniques in the LASSO regression setting.},
doi = {10.1002/qre.2262},
journal = {Quality and Reliability Engineering International},
number = 3,
volume = 34,
place = {United States},
year = {2018},
month = {1}
}

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