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The Comparison of Robust Partial Least Squares Regression Methods (RSIMPLS, PRM) with Robust Principal Component Regression for Predicting Tourist Arrivals to Turkey
Doğuş University Journal
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| Title |
The Comparison of Robust Partial Least Squares Regression Methods (RSIMPLS, PRM) with Robust Principal Component Regression for Predicting Tourist Arrivals to Turkey
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| Creator |
Polat, Esra
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| Subject |
İstatistik
multicollinearity; outliers; robust principal component regression; robust partial least squares regression; tourist arrivals C52 |
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| Description |
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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| Publisher |
Doğuş Üniversitesi
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| Contributor |
—
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| Date |
2019-01-31
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| Type |
info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion — — — |
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| Format |
application/pdf
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| Identifier |
http://journal.dogus.edu.tr/index.php/duj/article/view/1101
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| Source |
Doğuş University Journal; Cilt 20, Sayı 1 (2019): Ocak; 31-47
Doğuş Üniversitesi Dergisi; Cilt 20, Sayı 1 (2019): Ocak; 31-47 1308-6979 1302-6739 |
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| Language |
eng
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| Relation |
http://journal.dogus.edu.tr/index.php/duj/article/view/1101/pdf
http://journal.dogus.edu.tr/index.php/duj/article/downloadSuppFile/1101/486 |
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| Rights |
Telif Hakkı (c) 2019 Doğuş Üniversitesi Dergisi
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