Comparison of ANN and principal component analysis-multivariate linear regression models for predicting the river flow based on developed discrepancy ratio statistic

2010 ◽  
Vol 37 (8) ◽  
pp. 5856-5862 ◽  
Author(s):  
Roohollah Noori ◽  
Amir Khakpour ◽  
Babak Omidvar ◽  
Ashkan Farokhnia
Keyword(s):  
Principal Component Analysis ◽  
Linear Regression ◽  
Regression Models ◽  
River Flow ◽  
Principal Component ◽  
Component Analysis ◽  
Multivariate Linear Regression ◽  
Linear Regression Models ◽  
Discrepancy Ratio

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 Predicting Students' Academic Performance Using Multiple Linear Regression and Principal Component Analysis

2018 ◽  
Vol 26 (0) ◽  
pp. 170-176 ◽  
Author(s):  
Stephen J.H. Yang ◽  
Owen H.T. Lu ◽  
Anna Y.Q. Huang ◽  
Jeff C.H. Huang ◽  
Hiroaki Ogata ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Academic Performance ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Principal Component ◽  
Component Analysis

scholarly journals Multivariate Analysis of Risk Factors of the COVID-19 Pandemic in the Community of Madrid, Spain

2021 ◽  
Vol 18 (17) ◽  
pp. 9227
Author(s):  
Víctor Pérez-Segura ◽  
Raquel Caro-Carretero ◽  
Antonio Rua
Keyword(s):  
Risk Factors ◽  
Principal Component Analysis ◽  
Linear Models ◽  
Principal Component ◽  
Scientific Work ◽  
Component Analysis ◽  
Incidence Rates ◽  
Transmission Risk ◽  
Linear Regression Models ◽  
Multivariate Statistical

It has been more than one year since Chinese authorities identified a deadly new strain of coronavirus, SARS-CoV-2. Since then, the scientific work regarding the transmission risk factors of COVID-19 has been intense. The relationship between COVID-19 and environmental conditions is becoming an increasingly popular research topic. Based on the findings of the early research, we focused on the community of Madrid, Spain, which is one of the world’s most significant pandemic hotspots. We employed different multivariate statistical analyses, including principal component analysis, analysis of variance, clustering, and linear regression models. Principal component analysis was employed in order to reduce the number of risk factors down to three new components that explained 71% of the original variance. Cluster analysis was used to delimit the territory of Madrid according to these new risk components. An ANOVA test revealed different incidence rates between the territories delimited by the previously identified components. Finally, a set of linear models was applied to demonstrate how environmental factors present a greater influence on COVID-19 infections than socioeconomic dimensions. This type of local research provides valuable information that could help societies become more resilient in the face of future pandemics.

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