Role of Principal Component Analysis (PCA) in the Evaluation of Competitiveness of Small Firms

2014 ◽  
Vol 926-930 ◽  
pp. 3954-3957 ◽  
Author(s):  
Li Ping Xiao ◽  
Yang Liu
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Small Firms ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
Role Of Principal ◽  
The Index System ◽  
Dimension Reducing

Principal Component Analysis (PCA) is a method of multivariate statistical analysis and has been widely used in statistical and mathematical analysis. We use this method in the evaluation of competitiveness of small firms. Using the data of 30 small firms, we build the index system to evaluate competitiveness. Our results show that Principal Component Analysis (PCA) is useful in dimension reducing and we find that profitability, growth,size and human resource are important influencing factors in the competitiveness of small firms.

scholarly journals Economic Causes of Extremism Intellectual (Thoughts) Views of Graduate Students Using Multivariate Statistical Techniques

2016 ◽  
Vol 9 (7) ◽  
pp. 160
Author(s):  
Hasan Abdullah Al-Dajah
Keyword(s):  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Statistical Analysis ◽  
Graduate Students ◽  
Multivariate Statistical Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Statistical ◽  
The Impact

The present study investigated the impact of the economic reasons on the intellectual (thoughts) extremism, and the statement of the most important indicators in the economic factor that lead to extremism from the views of graduate students. The study problem based on the following question: What are economic factors leading to the extremism of the intellectual(Thoughts)? Correlation coefficient, Principal component analysis (PCA), varimax (F) rotated factor analysis, and dendrogram cluster analysis (DCA) were assessed for the economic impacts that leads to extremism(Thoughts). Multivariate statistical analysis of the dataset and correlation analysis suggested that the strong positive correlations are commonly associated in the poverty and lack of interest in remote areas for major cities Center. Multivariate statistical analysis such as principal component analysis, varimax rotated factor analysis, and dendrogram cluster analysis allowed the identification of three main factors controlling that lead to extremism from the views of graduate students. The extracted factors are as follows: low living expenses, poverty and substantial deprivation, and unequal opportunities and unemployment associations related to prevalence of corruption phase.

Principal component analysis for functional data

2018 ◽  
Author(s):  
Peter Hall
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Functional Data ◽  
Weighted Least Squares ◽  
Principal Component ◽  
Adaptive Methods ◽  
Component Analysis ◽  
Infinite Dimensional ◽  
Role Of Principal ◽  
Functional Linear Regression

This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.

scholarly journals Statistical Analysis of Mitochondrial Pathologies in Childhood: Identification of Deficiencies Using Principal Component Analysis

2000 ◽  
Vol 80 (7) ◽  
pp. 1019-1030 ◽  
Author(s):  
Thierry Letellier ◽  
Gilles Durrieu ◽  
Monique Malgat ◽  
Rodrigue Rossignol ◽  
Jaromir Antoch ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Statistical Analysis ◽  
Principal Component ◽  
Component Analysis

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

Multivariate statistical approaches for an early detection of foaming in a refinery SCOT unit

2018 ◽  
Vol 66 (8) ◽  
pp. 665-679
Author(s):  
Hassan Enam Al Mawla ◽  
Andreas Kroll
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Time Pressure ◽  
Principal Component ◽  
Component Analysis ◽  
Differential Pressure ◽  
Foam Formation ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Proper Actions

Abstract The formation of foam in amine units is an issue that plant operators and field personnel are confronted with on a regular basis. The inability to take proper actions in due time may result in plant downtime and increased emissions. Steep rises in differential pressure indicate foam formation, and are monitored manually in practice. Antifoaming agent is added in order to reduce foaming, but this is usually carried out under time pressure. Hence, plant operating authorities have expressed a strong interest in a data-driven solution capable of providing an early warning against foaming. The classical univariate alarm associated with differential pressure can be ineffective for foaming detection due to high misdetection rates and its lateness of detection. Modern univariate approaches based on pattern recognition techniques may not be suitable either for an early detection, as no universally distinctive features of differential pressure are observed prior to foaming in the present study. In this contribution, the multivariate statistical process monitoring approach based on principal component analysis (PCA) is applied to the early detection of foaming in a continuously operated Shell Claus Off-gas Treating (SCOT) unit of a major refinery in Germany. The results are extended to facilitate fully automated and adaptive modeling based on exponentially weighted recursive principal component analysis (EWRPCA).

scholarly journals A-49 Visual Memory Errors and Intelligence: The Role of Verbal Mediation

2019 ◽  
Vol 34 (6) ◽  
pp. 908-909
Author(s):  
K Hakinson ◽  
J Moses ◽  
J RIvera ◽  
A Guerra ◽  
M Davis ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Perceptual Organization ◽  
Visual Memory ◽  
Principal Component ◽  
Component Analysis ◽  
Spoken Language ◽  
Verbal Mediation ◽  
Memory Errors ◽  
Shared Variance

Abstract Objective Examine the relationship of verbal mediation with visual memory errors and intelligence to understand the role of spoken language on other assessment measures. Method Assessment records were obtained from a Veteran Affairs clinic for veterans (n=100) with diverse neuropsychiatric conditions who completed the Wechsler Adult Intelligence Scale, third edition (WAIS-III), Multilingual Aphasia Examination (MAE), and Benton Visual Retention Test (BVRT). A Principal Component Analysis (PCA) was used to examine the interrelationship among these assessments. The components of spoken language, types of errors on the BVRT, and the four factors of the WAIS-III were factored using the PCA to identify common sources of variance. Results A principal component analysis revealed a six-factor model explaining 68.16% of the shared variance among the WAIS-III factors, MAE components, and BVRT Errors. Omission errors loaded with Processing Speed and Controlled Word Association. Distortions and size errors loaded with Perceptual Organization. Size errors also loaded with Verbal Comprehension and Visual Naming. Misplacements loaded with Working Memory and Sentence Repetition. Misplacements, perseverations, and omissions loaded with the Token Test (a measure associated with auditory comprehension). Rotation errors loaded with Perceptual Organization. Conclusions Results indicated significant shared variance between visual memory errors, spoken language, and intelligence factors. This suggests that spoken language is involved in the process of visual memory, and deficits in spoken language may result in increased errors on visual memory tasks. Therefore, treatment recommendations for visual memory difficulties should take into consideration verbal capabilities and intelligence factors to better individualize treatment.

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