Influence plots for LASSO
- Pukyong National Univ., Busan (Korea). Dept. of Statistics
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
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.
- Research Organization:
- Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
- Sponsoring Organization:
- U.S. Department of Defense (DOD); National Research Foundation of Korea (NRF)
- Grant/Contract Number:
- AC52-06NA25396
- OSTI ID:
- 1337112
- Report Number(s):
- LA-UR--16-26817
- 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
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