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Title: Influence plots for LASSO

With many predictors in regression, fitting the full model can induce multicollinearity problems. Least Absolute Shrinkage and Selection Operation (LASSO) is useful when the effects of many explanatory variables are sparse in a high-dimensional dataset. Influential points can have a disproportionate impact on the estimated values of model parameters. Here, this paper describes a new influence plot that can be used to increase understanding of the contributions of individual observations and the robustness of results. This can serve as a complement to other regression diagnostics techniques in the LASSO regression setting. Using this influence plot, we can find influential points and their impact on shrinkage of model parameters and model selection. Lastly, we provide two examples to illustrate the methods.
Authors:
 [1] ;  [2]
  1. Pukyong National Univ., Busan (Korea). Dept. of Statistics
  2. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-16-26817
Journal ID: ISSN 0748-8017
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
Quality and Reliability Engineering International
Additional Journal Information:
Journal Name: Quality and Reliability Engineering International; Journal ID: ISSN 0748-8017
Publisher:
Wiley
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
U.S. Department of Defense (DOD); National Research Foundation of Korea (NRF)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; least absolute shrinkage and selection operation; influential observations; LASSO plot; LASSO influence plot; mathematics
OSTI Identifier:
1337112