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首页> 外文期刊>Journal of applied statistics >The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses
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The evaluation of socio-economic development of development agency regions in Turkey using classical and robust principal component analyses

机译：使用经典稳健的主成分分析法评估土耳其发展机构地区的社会经济发展

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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.
机译：在这项研究中，经典而稳健的主成分分析被用于评估发展机构区域的社会经济发展，这些机构为减少土耳其区域之间的发展差异而提供服务。由于区域的发展水平之间存在很大差异，因此出现了异常问题，因此使用了可靠的统计方法。同样，使用经典且鲁棒的统计方法来调查数据集中是否存在异常值。在经典的主成分分析中，观察数必须大于变量数。否则，协方差矩阵的行列式为零。在主成分分析的稳健方法（ROBPCA）中，这是一种在高维数据中进行主成分分析的稳健方法，即使变量的数量大于观察值的数量，也可以获得主成分。本文首先基于经典和鲁棒的散布矩阵，通过主成分分析对26个开发机构进行19个变量评估，然后使用ROBPCA方法对这26个开发机构进行46个变量评估。

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