Examination of Dimension Reduction Performances of PLSR and PCR Techniques in Data with Multicollinearity

GÜVEN G., ŞAMKAR H.

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE, cilt.43, ss.969-978, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s40995-018-0565-1
  • Dergi Adı: IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY TRANSACTION A-SCIENCE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.969-978
  • Anahtar Kelimeler: Multicollinearity, PLSR, PCR, RMSECV, SIMULTANEOUS SPECTROPHOTOMETRIC DETERMINATION, PRINCIPAL COMPONENTS REGRESSION, LEAST-SQUARES REGRESSION, MIXTURES, SAMPLES, NUMBER
  • Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

One of the common ways to cope with the multicollinearity problem in multiple regression analysis is to use dimension reduction techniques. Among these techniques, the present study focuses on the Partial Least Square Regression(PLSR) and the Principle Component Regression(PCR) techniques. The study tries to determine in which cases the two techniques give similar results and in which cases and to what extent they are different in terms of dimension reduction. For this purpose, the performance of the techniques is examined on two real dataset. In addition, a Monte Carlo simulation is made to evaluate the performances of these techniques based on the criterion of Root Mean Square Error of Cross Validation(RMSECV) under different conditions.