Nonparametric principal components regression

Jennifer Umali, Erniel Barrios

Research output: Contribution to journalArticleResearchpeer-review

13 Citations (Scopus)

Abstract

Principal components regression (PCR) is used in resolving the multicollinearity problem but specification bias occurs due to the selection only of the important principal components to be included resulting in the deterioration of predictive ability of the model. We propose the PCR in a nonparametric framework to address the multicollinearity problem while minimizing the specification bias that affects predictive ability of the model. The simulation study illustrated that nonparametric PCR addresses the multicollinearity problem while retaining higher predictive ability relative to parametric principal components regression model.

Original languageEnglish
Pages (from-to)1797-1810
Number of pages14
JournalCommunications in Statistics - Simulation and Computation
Volume43
Issue number7
DOIs
Publication statusPublished - 2014
Externally publishedYes

Keywords

  • High-dimensional data
  • Multicollinearity
  • Nonparametric regression
  • Principal components regression

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