Study on the Effect Factors of Non-Perormance Loan Ratio of Chinese Commerical Banks

2011 ◽  
Vol 50-51 ◽  
pp. 728-732
Author(s):  
Ping Li ◽  
Ming Ying Zhuo ◽  
Li Chao Feng ◽  
Rui Zhang
Keyword(s):  
Principal Component Analysis ◽  
Commercial Banks ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Method ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Effect Factors ◽  
Financial Indicator ◽  
Commerical Banks

Non-performance loan ratio is one of the important assessment criteria of the security of credit assets. It is also an important financial indicator to evaluate the general strength of commercial banks. Using principal component analysis method and statistical software SPSS16.0 and based on the non-performance loan ratio and relative data of some commercial banks in China in 2007, this paper provided a principal component analysis model for the non-performance loan ratio of China’s commercial banks. The factors that affect the non-performance loan ratio were refined in this paper. Finally, the characteristics of effect factors of each bank were analyzed and compared in detail.

scholarly journals Perceived difficulties and complications in learners of phacoemulsification: A principal component analysis model

2019 ◽  
Vol 67 (2) ◽  
pp. 213 ◽  
Author(s):  
Rohit Saxena ◽  
Sagnik Sen ◽  
Mukesh Patil ◽  
Atul Kumar ◽  
SreelakshmiP Amar ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Model ◽  
Principal Component Analysis Model

Alternative butanol/gasoline and butanol/diesel fuel blends: An analysis of the interdependence between physical-chemical properties by a multivariate principal component analysis model

2018 ◽  
Vol 31 (5) ◽  
pp. 733-754
Author(s):  
Luiz Filipe Paiva Brandão ◽  
Jez Willian Batista Braga ◽  
Paulo Anselmo Ziani Suarez
Keyword(s):  
Principal Component Analysis ◽  
Diesel Fuel ◽  
Boiling Point ◽  
Principal Component ◽  
Component Analysis ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Initial Boiling Point ◽  
Fuel Blends ◽  
Physical Chemical

The use of butanol as an oxygenated component in blends with fossil fuels has recently been recognized by the industry as a promising and green alternative for automotive use, being subject of several recent studies. In this work, the interdependence between important physical-chemical properties of butanol/gasoline and butanol/diesel fuel blends was investigated using a multivariate principal component analysis model. The model dataset was based on laboratorial results of density, kinematic viscosity, distillation, vapor pressure, octane rating, anti-knock index, flash point and cetane number in a total of 48 blends, the variables of which were transformed to principal component analysis matrix representations, pre-processed and then analyzed. A good coherence was observed between the experimental results in laboratory and those derived from the principal component analysis models, evidencing important physical-chemical changes in blends’ properties due to the butanol addition. Principal component analysis scores and loadings plots could provide an intuitive and comprehensive data visualization. Butanol/gasoline fuel blends showed an overall increase in density, octane rating and higher distillation temperatures from the initial boiling point to T60 (temperature of the 60% distilled volume) and reduction of the distillation temperatures from T70 to the final boiling point. An absolute reduction in values of all properties was observed for butanol/diesel fuel blends, especially for initial distillation temperatures from initial boiling point to T35, T98, final boiling point and flash point, whereas the reductions for density, kinematic viscosity and cetane number were less intense. Total variances of up to 92.50% and 94.14% were explained by the proposed principal component analysis model, depending on the blends matrix and butanol isomer composition.

MIXTURE PRINCIPAL COMPONENT ANALYSIS MODEL FOR MULTIVARIATE PROCESSES MONITORING

2005 ◽  
Vol 04 (02) ◽  
pp. 151-166
Author(s):  
FENG ZHANG ◽  
ZHUJUN WENG
Keyword(s):  
Principal Component Analysis ◽  
Process Monitoring ◽  
Measurement Data ◽  
Principal Component ◽  
Likelihood Estimation ◽  
Component Analysis ◽  
Distribution Model ◽  
Analysis Model ◽  
Principal Component Analysis Model ◽  
Statistical Process

A mixture probabilistic principal component analysis model is proposed as a process monitoring tool in this paper. High-dimensional measurement data could be aggregated into some clusters based on the mixture distribution model, where the number of these clusters are automatically determined from the maximum likelihood estimation procedures. It was illustrated that the mixture PCA models conform to the multivariate data well in the experiments involving Gaussian mixtures. The multivariate statistical process monitoring mechanism is then developed first with the learning of a finite mixture model with variant principal component within each cluster, followed by the construction of the statistical process confidence intervals for the identified regions or nodes from T2 charts. For the abnormal input measurement, they would fall out of the acceptance region set by the confidence control limits.

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