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http://hdl.handle.net/10609/136948
Title: | Variable selection in regression models using global sensitivity analysis |
Author: | Becker, William Paruolo, Paolo saltelli, andrea |
Others: | Universitat Oberta de Catalunya University of Bergen |
Citation: | Becker, W., Paruolo, P. & Saltelli, A. (2021). Variable Selection in Regression Models Using Global Sensitivity Analysis. Journal of Time Series Econometrics, 13(2), 187-233. https://doi.org/10.1515/jtse-2018-0025 |
Abstract: | Global sensitivity analysis is primarily used to investigate the effects of uncertainties in the input variables of physical models on the model output. This work investigates the use of global sensitivity analysis tools in the context of variable selection in regression models. Specifically, a global sensitivity measure is applied to a criterion of model fit, hence defining a ranking of regressors by importance; a testing sequence based on the ¿Pantula-principle¿ is then applied to the corresponding nested submodels, obtaining a novel model-selection method. The approach is demonstrated on a growth regression case study, and on a number of simulation experiments, and it is found competitive with existing approaches to variable selection. |
Keywords: | simulation model selection sensitivity analysis Monte Carlo |
DOI: | 10.1515/jtse-2018-0025 |
Document type: | info:eu-repo/semantics/article |
Issue Date: | 15-Mar-2021 |
Publication license: | http://creativecommons.org/licenses/by/3.0/es/ |
Appears in Collections: | Articles Articles cientÍfics |
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File | Description | Size | Format | |
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Variable Selection in Regression Models Using Global Sensitivity Analysis.pdf | 1,3 MB | Adobe PDF | View/Open |
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