An alternative approach to reduce dimensionality in data envelopment analysis

Grace Lee Ching Yap, Wan Rosmanira Ismail, Zaidi Isa

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Principal component analysis reduces dimensionality; however, uncorrelated components imply the existence of variables with weights of opposite signs. This complicates the applicationin data envelopment analysis. To overcome problems due to signs, a modification to the component axes is proposed and was verified using Monte Carlo simulations.

Original languageEnglish
Pages (from-to)128-147
Number of pages20
JournalJournal of Modern Applied Statistical Methods
Volume12
Issue number1
Publication statusPublished - 2013

Fingerprint

Data Envelopment Analysis
Dimensionality
Alternatives
Principal Component Analysis
Monte Carlo Simulation
Imply
Data envelopment analysis
Principal component analysis
Monte Carlo simulation

Keywords

  • Data envelopment analysis
  • Monte Carlo simulation
  • Principal component analysis
  • Redundancy analysis

ASJC Scopus subject areas

  • Statistics, Probability and Uncertainty
  • Statistics and Probability

Cite this

An alternative approach to reduce dimensionality in data envelopment analysis. / Yap, Grace Lee Ching; Ismail, Wan Rosmanira; Isa, Zaidi.

In: Journal of Modern Applied Statistical Methods, Vol. 12, No. 1, 2013, p. 128-147.

Research output: Contribution to journalArticle

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