Soto, G.M., 2004, Using Principal Component Analysis to Explain Term Structure Movements: Performance and Stability, in: Tavidze, A. (Ed.) Progress in Econmics Research, Volume 8. Nova Science Publishers, New York.Soto,Gloria M."Using Principal Component Analysis to Explain Term Structure ...
4g). We also performed principal component analysis and calculated the Mahalanobis distances of the principal axes for the three bond types36, which are statistically separated in the catch-bond intensity vs Mahalanobis distance plot (Fig. 4h). Interestingly, WC and SC bonds show distinct ...
principal component analysisdiscriminant analysispartial-least squaresA 3-D chemistry-transport model has been applied to the Mexico City metropolitan area to investigate the origin of elevated levels of non-fossil (NF) carbonaceous aerosols observed in this highly urbanized region. High time resolution ...
The Study cohort status and the first 20 principal components from a principal-component analysis of the genome-wide genotypes were fitted as covariates in the model to control for the effects attributable to population structure. Analyses were repeated (i) without PC adjustment, (ii) adjusting ...
Cluster analysis We applied principal component analysis (PCA) to the proportion of variants in each quintile of the gene transcript, separated by variant class, using the SKLearn “decomposition.PCA” function. PCA loadings were calculated based on locations of synonymous, missense, and all pLoF ...
aThe fourth principal component reflects enterprise debt-repaying ability, each enterprise scoring distribution also is even, explain each enterprise has certain solvency and all can better maintain enterprise operation. 第四主要成分反射企业债务回报的能力,每企业计分的发行也是均匀的,解释每企业有某一偿付...
The statistical technique used was an exploratory factorial analysis with an extracted principal component. The Statistics Hypothesis: Ho : p = 0 has no correlation, while Ha : p 鈮 0 does. Statistics test to prove: X2, Bartlett's test of sphericity, KMO (Kaiser-Meyer-Olkin), Measure of...
We use principal component analysis (PCA) to identify three key factors shaping spatial inequality of property flood risk: development density, centrality and segregation, and economic activity. We then develop a classification and regression tree (CART) model to examine ways these factors interact in...
we flip the sign for RMSE and MAE and examine negative RMSE and negative MAE. Within each fold, we applied principal component analysis (PCA) to reduce the dimensionality ofZto the 9 PCs that explained ≥1% variance in the data. Additionally, age and sex were controlled by regressing their ...
Established principles from the concept of performance and performance analysis of the composition and indicators to start, explained the purpose and significance of our performance analysis, analysis of the listed commercial banks based on principal component analysis methods of empirical research, ...