Communications for Statistical Applications and Methods

  • 제21권4호
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  • Pages.349-361
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  • 2014
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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Comparison of Lasso Type Estimators for High-Dimensional Data

  • Kim, Jaehee (Department of Statistics, Duksung Women's University)
  • 투고 : 2014.06.06
  • 심사 : 2014.07.14
  • 발행 : 2014.07.31
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초록

This paper compares of lasso type estimators in various high-dimensional data situations with sparse parameters. Lasso, adaptive lasso, fused lasso and elastic net as lasso type estimators and ridge estimator are compared via simulation in linear models with correlated and uncorrelated covariates and binary regression models with correlated covariates and discrete covariates. Each method is shown to have advantages with different penalty conditions according to sparsity patterns of regression parameters. We applied the lasso type methods to Arabidopsis microarray gene expression data to find the strongly significant genes to distinguish two groups.

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참고문헌

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