Yazar "Alkan, B. Baris" seçeneğine göre listele

Listeleniyor 1 - 3 / 3
  • [ X ]
    Öğe
    A comparison of different procedures for principal component analysis in the presence of outliers
    (Taylor & Francis Ltd, 2015) Alkan, B. Baris; Atakan, Cemal; Alkan, Nesrin
    Principal component analysis (PCA) is a popular technique that is useful for dimensionality reduction but it is affected by the presence of outliers. The outlier sensitivity of classical PCA (CPCA) has caused the development of new approaches. Effects of using estimates obtained by expectation-maximization - EM and multiple imputation - MI instead of outliers were examined on the artificial and a real data set. Furthermore, robust PCA based on minimum covariance determinant (MCD), PCA based on estimates obtained by EM instead of outliers and PCA based on estimates obtained by MI instead of outliers were compared with the results of CPCA. In this study, we tried to show the effects of using estimates obtained by MI and EM instead of outliers, depending on the ratio of outliers in data set. Finally, when the ratio of outliers exceeds 20%, we suggest the use of estimates obtained by MI and EM instead of outliers as an alternative approach.
  • [ X ]
    Öğe
    A useful approach to identify the multicollinearity in the presence of outliers
    (Taylor & Francis Ltd, 2015) Sinan, Alper; Alkan, B. Baris
    The presence of outliers in the data sets affects the structure of multicollinearity which arises from a high degree of correlation between explanatory variables in a linear regression analysis. This affect could be seen as an increase or decrease in the diagnostics used to determine multicollinearity. Thus, the cases of outliers reduce the reliability of diagnostics such as variance inflation factors, condition numbers and variance decomposition proportions. In this study, we propose to use a robust estimation of the correlation matrix obtained by the minimum covariance determinant method to determine the diagnostics of multicollinearity in the presence of outliers. As a result, the present paper demonstrates that the diagnostics of multicollinearity obtained by the robust estimation of the correlation matrix are more reliable in the presence of outliers.
  • [ X ]
    Öğe
    Use of biplot technique for the comparison of the missing value imputation methods
    (Inderscience Enterprises Ltd., 2015) Alkan, B. Baris; Alkan, Nesrin; Atakan, Cemal; Terzi, Yuksel
    This study was performed to assess the effects of different imputation methods on the performance of a biplot technique. We selected the Fisher's iris data as our reference dataset. Some elements of the Iris data were deleted in different rates under missing at random (MAR) assumption to generate incomplete datasets which had 3.5%, 7%, %15, 20% missing value. Datasets with missing values were completed by four imputation methods [mean imputation, regression imputation, expectation maximisation (EM) algorithm, multiple imputation (MI)]. The new imputed datasets were analysed by biplot technique and their results were compared with original complete biplot of the data. The results of biplot analysis were similar in all the imputation methods when missing rate is low under MAR assumption. Even when the missing rate was greater than 10%, results of EM and MI methods were similar to real values and graphical representation of original data. For multivariate methods, we also propose filling in the missing value with the arithmetic mean of the imputed estimates which are obtained with multiple imputation. This paper also indicates that the use of biplot technique for the comparison of the missing value imputation methods provides a useful visual tool. © 2015 Inderscience Enterprises Ltd.