Principal Component Analysis (PCA) is a common procedure for the analysis of financial market data, such as implied … volatility smiles or interest rate curves. Recently, Pelsser and Lord [11] raised the question whether PCA results may not be … with foreign exchange option markets. For this matrix structure, PCA effects which are interpreted as shift, skew and …

. Bezugspunkte sind dabei das klassische Verfahren der Hauptkomponentenanalyse sowie als Alternativmethode die geographisch … gewichtete Hauptkomponentenanalyse. Im methodischen Vergleich zeigt sich, dass die Alternative - auch aufgrund des hohen … Literatur vorzufindende Vorgehensweise aus Hauptkomponentenanalyse, Clusteranalyse und Diskriminanzanalyse gut abgrenzbare und …

In this paper, we compare Principal Component Analysis (PCA) and Partial Least Squares (PLS) methods to generate … that differ substantially from those obtained with PCA, increasing the composite indices' predictive performance for the …

Analysis (PCA) algorithms. The performance of different treatments is compared in the extensive simulation study under several … typical data generating processes and recommendations are made. An application of PLS and PCA algorithms with non …

In this paper the Social Institutions and Gender Index (SIGI) is constructed with Principal Component Analysis (PCA … weighting procedures using PCA and PLS. We find that gender inequality in social institutions has significant effect on …

Covariance matrix estimation and principal component analysis (PCA) are two cornerstones of multivariate analysis …

power when high numbers of variables but only limited observations are available. We apply PCA-DEA for radial efficiency … while maintaining most of the variation in the original data. Our results suggest that the PCA-DEA methodology reduces the …