The Comparison of Robust Partial Least Squares Regression Methods (RSIMPLS, PRM) with Robust Principal Component Regression for Predicting Tourist Arrivals to Turkey | Zendy

Esra Polat | Zendy

Open Access

The Comparison of Robust Partial Least Squares Regression Methods (RSIMPLS, PRM) with Robust Principal Component Regression for Predicting Tourist Arrivals to Turkey

Author(s) -

Esra Polat

Publication year - 2019

Publication title -

doğuş üniversitesi dergisi

Language(s) - English

Resource type - Journals

eISSN - 1308-6979

pISSN - 1302-6739

DOI - 10.31671/dogus.2019.415

Subject(s) - partial least squares regression , robust regression , principal component regression , principal component analysis , regression , statistics , regression analysis , total least squares , mathematics , linear regression , component (thermodynamics) , econometrics , physics , thermodynamics

Tourism is one of the most important component in the economic development strategy of many developing countries such as Turkey. The annual data set of Turkey (1986 - 2013), including the six factors affecting the tourist arrivals, is examined. The aim of this study is modelling the tourist arrivals to Turkey in cases of both multicollinearity and outlier existence in the data set by using a robust Principal Component Regression method: RPCR, two robust Partial Least Squares Regression methods: RSIMPLS and Partial Robust M-Regression (PRM). Hence, the best model giving the best predictions of tourist arrivals is selected and the most important factors are determined.

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