Significance analysis of input variables using bootstrap method for elastic net under sampling uncertainty

Hansu Kim, Tae Hee Lee

Research output: Contribution to journalArticlepeer-review

Abstract

The problem of high-dimensional input variables can be solved by analyzing the significance of input variables using an elastic net. However, the significance varies because of sampling uncertainty, which can lead to incorrect inferences. Specifically, the sampling uncertainty tends to increase as the dimension of input variables increases. Therefore, a significance analysis method using bootstrap method for the elastic net is presented herein to reduce the sampling uncertainty. Through a mathematical example, the proposed significance analysis method was confirmed to provide greater accuracy than the elastic net. Additionally, the significance of input variables was analyzed by applying the proposed significance analysis method to an engineering problem. Although the bootstrap method entails high computational costs, it is expected that meaningful results can be obtained at a reasonable cost.

Original languageEnglish
Pages (from-to)141-148
Number of pages8
JournalTransactions of the Korean Society of Mechanical Engineers, A
Volume45
Issue number2
DOIs
StatePublished - 2021

Keywords

  • Bootstrap Method
  • Elastic Net
  • High-Dimensional Input Variables
  • Sampling Uncertainty
  • Significance Analysis

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