ArticleCommunications in statistics: theory and methods2018
Estimation of Sobol's Sensitivity Indices under Generalized Linear Models.
Article in Communications in statistics: theory and methods, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Long non-coding RNA LINC02499 regulates the expression of CYP3A5 in human liver cells.Archives of toxicology · 2025Article
- Environmental determinants predicting population vulnerability to high yellow fever incidence.Royal Society open science · 2022Article
- Estimating the effect of non-pharmaceutical interventions to mitigate COVID-19 spread in Saudi Arabia.BMC medicine · 2022Article
- Transcriptional Regulation of Carboxylesterase 1 in Human Liver: Role of the Nuclear Receptor Subfamily 1 Group H Member 3 and Its Splice Isoforms.Drug metabolism and disposition: the biological fate of chemicals · 2022Article
- High dimensional model representation of log likelihood ratio: binary classification with SNP data.BMC medical genomics · 2020Article
- High dimensional model representation of log-likelihood ratio: binary classification with expression data.BMC bioinformatics · 2020Article
- Explaining income-related inequalities in cardiovascular risk factors in Tunisian adults during the last decade: comparison of sensitivity analysis of logistic regression and Wagstaff decomposition analysis.International journal for equity in health · 2019Article
- Ligand-Free Estrogen ReceptorMolecular pharmacology · 2019Article
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4 authors.
Funding
Abstract
We derive explicit formulas for Sobol's sensitivity indices (SSIs) under the generalized linear models (GLMs) with independent or multivariate normal inputs. We argue that the main-effect SSIs provide a powerful tool for variable selection under GLMs with identity links under polynomial regressions. We also show via examples that the SSI-based variable selection results are similar to the ones obtained by the random forest algorithm but without the computational burden of data permutation. Finally, applying our results to the problem of gene network discovery, we identify though the SSI analysis of a public microarray dataset several novel higher-order gene-gene interactions missed out by the more standard inference methods. The relevant functions for SSI analysis derived here under GLMs with identity, log, and logit links are implemented and made available in the R package
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