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Estimation of Sobol's sensitivity indices under generalized linear models

Author

Listed:
  • Rong Lu
  • Danxin Wang
  • Min Wang
  • Grzegorz A. Rempala

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 through 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 Sobol sensitivity.

Suggested Citation

  • Rong Lu & Danxin Wang & Min Wang & Grzegorz A. Rempala, 2018. "Estimation of Sobol's sensitivity indices under generalized linear models," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(21), pages 5163-5195, November.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:21:p:5163-5195
    DOI: 10.1080/03610926.2017.1388397
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