In most of applied disciplines, many variables are sometimes measured on each individual, which result a huge data set consisting of large number of variables, say p [Sharma (1996)]. Using this collected data set in any statistical analysis may cause several troubles
<p>The first four principal components (PCs) of a PCA for summary statistics calculated for 10,000 s...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
<p>Principal component analysis is used across different disciplines, giving rise to a diverse termi...
data In this article, we introduce a procedure for selecting variables in principal components analy...
In many large environmental datasets redundant variables can be discarded without the loss of extra ...
The paper provides various interpretations of principal components in the analysis of multiple measu...
Principal Component Analysis (PCA) is one of the most popular techniques in multivariate statistica...
This thesis is concerned with the problem of selection of important variables in Principal Component...
The theory and practice of principal components are considered both from the point of view of statis...
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN030882 / BLDSC - British Library D...
As part of Exploratory Analysis of Multivariate data, Principal Componet Analysis (PCA) is generally...
Ree, Carretta, and Teachout (2015) raise the need for further investigation into dominant general fa...
Principal component analysis (PCA) is an exploratory statistical method for graphical description of...
Principal component analysis is a method of statistical anal- ysis used to reduce the dimensionality...
Statisticians have wrestled with the question of sample size in exploratory factor analysis and..pri...
<p>The first four principal components (PCs) of a PCA for summary statistics calculated for 10,000 s...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
<p>Principal component analysis is used across different disciplines, giving rise to a diverse termi...
data In this article, we introduce a procedure for selecting variables in principal components analy...
In many large environmental datasets redundant variables can be discarded without the loss of extra ...
The paper provides various interpretations of principal components in the analysis of multiple measu...
Principal Component Analysis (PCA) is one of the most popular techniques in multivariate statistica...
This thesis is concerned with the problem of selection of important variables in Principal Component...
The theory and practice of principal components are considered both from the point of view of statis...
SIGLEAvailable from British Library Document Supply Centre-DSC:DXN030882 / BLDSC - British Library D...
As part of Exploratory Analysis of Multivariate data, Principal Componet Analysis (PCA) is generally...
Ree, Carretta, and Teachout (2015) raise the need for further investigation into dominant general fa...
Principal component analysis (PCA) is an exploratory statistical method for graphical description of...
Principal component analysis is a method of statistical anal- ysis used to reduce the dimensionality...
Statisticians have wrestled with the question of sample size in exploratory factor analysis and..pri...
<p>The first four principal components (PCs) of a PCA for summary statistics calculated for 10,000 s...
Analysis of data sets that involve large numbers of variables usually entails some type of model fit...
<p>Principal component analysis is used across different disciplines, giving rise to a diverse termi...