In this paper, 13 different types of composite indices are constructed by linear combination of indicator variables (with and without outliers/data corruption). Weights of different indicator variables are obtained by maximizing the sum of squared (and, alternatively, absolute) correlation...

Effects of outliers on mean, standard deviation and Pearson's correlation coefficient are well known. The Principal Components analysis uses Pearson's product moment correlation coefficients to construct composite indices from indicator variables and hence may be very sensitive to effects of...

Construction of (composite) indices by the Principal Component Analysis (PCA) is very common, but this method has a preference for highly correlated variables to the poorly correlated variables in the dataset. However, poor correlation does not entail marginal importance, since correlation...

In this paper we construct thirteen different types of composite indices by linear combination of indicator variables (with and without outliers/data corruption). Weights of different indicator variables are obtained by maximization of the sum of squared (and, alternatively, absolute)...

Construction of (composite) indices by the PCA is very common, but this method has a preference for highly correlated variables to the poorly correlated variables in the data set. However, poor correlation does not entail the marginal importance, since correlation coefficients among the...