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

Bulut, Hasan; Öner, Yüksel

doi:10.1080/02664763.2016.1267115

Cited by 9 publications

(4 citation statements)

References 9 publications

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“…It is noted that g � n(1 − α t ) where α t is representing the ratio of trimming. Moreover, the threshold point of MCD is equivalent to α t [22]. Te second is the minimum volume ellipsoid (MVE) estimator, i.e.…”

Section: Robust Regression and Mean Estimatorsmentioning

confidence: 99%

Särndal Approach and Separate Type Quantile Robust Regression Type Mean Estimators for Nonsensitive and Sensitive Variables in Stratified Random Sampling

Shahzad

Ahmad

Al‐Noor

et al. 2022

Journal of Mathematics

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Surprising perceptions may happen in survey sampling. The arithmetic mean estimator is touchy to extremely enormous or potentially small observations, whenever selected in a sample. It can give one-sided (biased) results and eventually, enticed to erase from the selected sample. These extremely enormous or potentially small observations, whenever known, can be held in the sample and utilized as supplementary information to expand the exactness of estimates. Also, a supplementary variable is consistently a well-spring of progress in the exactness of estimates. A suitable conversion/transformation can be utilized for getting much more precise estimates. In the current study, regarding population mean, we proposed a robust class of separate type quantile regression estimators under stratified random sampling design. The proposed class is based on extremely enormous or potentially small observations and robust regression tools, under the framework of Särndal. The class is at first defined for the situation when the nature of the study variable is nonsensitive, implying that it bargains with subjects that do not create humiliation when respondents are straightforwardly interrogated regarding them. Further, the class is stretched out to the situation when the study variable has a sensitive nature or theme. Sensitive and stigmatizing themes are hard to explore by utilizing standard information assortment procedures since respondents are commonly hesitant to discharge data concerning their own circle. The issues of a population related to these themes (for example homeless and nonregular workers, heavy drinkers, assault and rape unfortunate casualties, and drug users) contain estimation errors ascribable to nonresponses as well as untruthful revealing. These issues might be diminished by upgrading respondent participation by scrambled response devices/techniques that cover the genuine value of the sensitive variable. Thus, three techniques (namely additive, mixed, and Bar-Lev) are incorporated for the purposes of the article. The productivity of the proposed class is also assessed in light of real-life dataset. Lastly, a simulation study is also done to determine the performance of estimators.

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Section: Robust Regression and Mean Estimatorsmentioning

confidence: 99%

Särndal Approach and Separate Type Quantile Robust Regression Type Mean Estimators for Nonsensitive and Sensitive Variables in Stratified Random Sampling

Shahzad

Ahmad

Al‐Noor

et al. 2022

Journal of Mathematics

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“…These new variables are called principal components. However, it is well known that classical PCA is sensitive to outliers [20]. A robust principal component analysis method called ROBPCA was developed [22].…”

Section: Principal Component Analysismentioning

confidence: 99%

“…• Stage 3: The data points are projected on this subspace where their location and scatter matrix are robustly estimated, from which its nonzero eigenvalues 1 , 2 , … , are computed. The corresponding eigenvectors are the robust principal components [20,22] Principal component scores are obtained from (1):…”

Section: Principal Component Analysismentioning

confidence: 99%

“…Moreover, Bulut and Öner used robust PCA to obtain a composite index that is not sensitive to outliers. Thus, they evaluated the regions robustly in Turkey about their socioeconomic development [20]. Also, Alpaykut investigated the well-being of cities in Turkey by using classical PCA, which is sensitive to outliers, and TOPSIS methods [21].…”

Section: Introductionmentioning

confidence: 99%

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A Robust Alternative to Environmental Performance Index

Bulut

2021

Journal of New Theory

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A composite index called the Environmental Performance Index (EPI) was obtained to evaluate countries' environmental performance. This index was calculated for 180 countries concerning 24 environmental indicators. However, it is well known that there are huge differences between countries regarding environmental factors besides social, economic, and cultural factors. This case aggravates the doubt that the data set has outliers. Therefore, the index values should be obtained such that they are unsensitive to outliers. This study aims to generate a composite index, which is a robust alternative to EPI. For this aim, we use the Robust Principal Component Analysis (ROBPCA) and the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS), which is a multicriteria decision-making method.

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