Comparison of PLSR and PCR techniques in terms of dimension reduction: an application on internal migration data in Turkey

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Atıf İçin Kopyala

ŞAMKAR H., GÜVEN G.

INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES, cilt.3, sa.8, ss.7-13, 2016 (ESCI)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 3 Sayı: 8
• Basım Tarihi: 2016
• Doi Numarası: 10.21833/ijaas.2016.08.002
• Dergi Adı: INTERNATIONAL JOURNAL OF ADVANCED AND APPLIED SCIENCES
• Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Directory of Open Access Journals
• Sayfa Sayıları: ss.7-13
• Eskişehir Osmangazi Üniversitesi Adresli: Evet

Özet

Partial Least Squares Regression (PLSR) and Principle Component Regression (PCR) are dimension reduction techniques especially used in the presence of multicollinearity. In this study, these two techniques are described and their performance is compared in terms of dimension reduction. Root Mean Square Error of Cross Validation (RMSECV) is used as comparison criteria. PLSR and PCR techniques are applied on internal migration data in Turkey and it is found that PLSR technique is superior to PCR in terms of dimension reduction. (C) 2016 The Authors. Published by IASE.