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The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses

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  • Hasan Bulut
  • Yüksel Öner

Abstract

In this study, classical and robust principal component analyses are used to evaluate socioeconomic development of regions of development agencies that give service on the purpose of decreasing development difference among regions in Turkey. Due to the high differences between development levels of regions outlier problem occurs, hence robust statistical methods are used. Also, classical and robust statistical methods are used to investigate if there are any outliers in data set. In classic principal component analyse, the number of observations must be larger than the number of variables. Otherwise determinant of covariance matrix is zero. In Robust method for Principal Component Analysis (ROBPCA), a robust approach to principal component analyse in high-dimensional data, even if the number of variables is larger than the number of observations, principal components are obtained. In this paper, firstly 26 development agencies are evaluated with 19 variables by using principal component analysis based on classical and robust scatter matrices and then these 26 development agencies are evaluated with 46 variables by using the ROBPCA method.

Suggested Citation

  • Hasan Bulut & Yüksel Öner, 2017. "The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(16), pages 2936-2948, December.
    • Handle: RePEc:taf:japsta:v:44:y:2017:i:16:p:2936-2948
    DOI: 10.1080/02664763.2016.1267115
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    1. Hasan Bulut & Yüksel Öner, 2015. "The Evaluation of The Development Agency Regions In Turkey In Terms of Some Socioeconomic Indicator with Factor Analysis," Alphanumeric Journal, Bahadir Fatih Yildirim, vol. 3(1), pages 81-88, June.
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