Communications for Statistical Applications and Methods

  • 제30권4호
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  • Pages.355-368
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  • 2023
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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ELCIC: An R package for model selection using the empirical-likelihood based information criterion

  • Chixiang Chen (Division of Biostatistics and Bioinformatics, University of Maryland School of Medicine) ;
  • Biyi Shen (Regeneron Pharmaceuticals) ;
  • Ming Wang (Department of Population and Quantitative Health Sciences, Case Western Reserve University)
  • 투고 : 2022.10.05
  • 심사 : 2023.05.12
  • 발행 : 2023.07.31
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초록

This article introduces the R package ELCIC (https://cran.r-project.org/web/packages/ELCIC/index.html), which provides an empirical likelihood-based information criterion (ELCIC) for model selection that includes, but is not limited to, variable selection. The empirical likelihood is a semi-parametric approach to draw statistical inference that does not require distribution assumptions for data generation. Therefore, ELCIC is more robust and versatile in the context of model selection compared to the currently existing information criteria. This paper illustrates several applications of ELCIC, including its use in generalized linear models, generalized estimating equations (GEE) for longitudinal data, and weighted GEE (WGEE) for missing longitudinal data under the mechanisms of missing at random and dropout.

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