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Title: Assessing robustness of factor ranking for supersaturated designs

Journal Article · · Quality and Reliability Engineering International
DOI:https://doi.org/10.1002/qre.2262· OSTI ID:1467262
 [1]; ORCiD logo [2]
  1. Pukyong National Univ., Busan (Korea). Dept. of Statistics
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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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.

Research Organization:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE; USDOD; National Research Foundation of Korea (NRF)
Grant/Contract Number:
AC52-06NA25396; 2017R1D1A3B03028648
OSTI ID:
1467262
Report Number(s):
LA-UR-17-24159
Journal Information:
Quality and Reliability Engineering International, Vol. 34, Issue 3; ISSN 0748-8017
Publisher:
WileyCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 1 work
Citation information provided by
Web of Science

References (8)

Analysis of supersaturated designs via the Dantzig selector journal July 2009
Examining robustness of model selection with half-normal and LASSO plots for unreplicated factorial designs journal April 2017
Application of strategic sample composition to the screening of anti-inflammatory drugs in water samples using solid-phase microextraction journal October 2004
Bayesian D-optimal supersaturated designs journal January 2008
Discussion: The Dantzig selector: Statistical estimation when p is much larger than n journal December 2007
Influence Plots for LASSO journal November 2016
Construction of supersaturated designs through partially aliased interactions journal January 1993
DASSO: connections between the Dantzig selector and lasso journal January 2009

Figures / Tables (22)

Table 1(p. 3)table Table 1
Table 2(p. 4)table Table 2
Figure 1(p. 5)figure Figure 1
Figure 2(p. 5)figure Figure 2
Figure 3(p. 6)figure Figure 3
Figure 4(p. 7)figure Figure 4
Table 3(p. 7)table Table 3
Figure 5(p. 8)figure Figure 5
Figure 6(p. 9)figure Figure 6
Table 4(p. 9)table Table 4
Figure 7(p. 10)figure Figure 7
Figure 8(p. 11)figure Figure 8
Figure 10(p. 12)figure Figure 10
Figure 9(p. 12)figure Figure 9
Figure 11(p. 13)figure Figure 11
Figure 12(p. 14)figure Figure 12
Table 5(p. 14)table Table 5
Figure 13(p. 15)figure Figure 13
Figure 14(p. 15)figure Figure 14
Table 6(p. 16)table Table 6
Figure 15(p. 17)figure Figure 15
Figure 16(p. 18)figure Figure 16

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Related Subjects

97 MATHEMATICS AND COMPUTING
Supersaturated designs
influential observations
LASSO influence plots
LASSO variable selection ranking plots
3-D LASSO influence plots