Principal variables selection in multivariate analysis
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Date
2007
Authors
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Publisher
CCSHAU
Abstract
Practical as well as theoretical considerations compel the researchers dealing
with huge data sets to select principal variables or to discard the redundant variables.
Selection or discarding of variables simplifies the analysis and also makes the interpretations
of the results easier. In the present study, we discussed and critically reviewed various variable
selection and variable discarding procedures. In particular we emphasized on determining the
subset of principal variables which provided maximum information using the concept of
Principal Component Analysis and that preserved the group structure of the data using
Procrustes Analysis. We also made use of generalized dependence and multivariate
association based on Canonical Correlation Analysis for selection of principal variables.
Comparative study of various variables selection procedures was made using three similarity
measures viz., RV- Coefficient, Jolliffe similarity and percentage of variation explained.
Empirical comparison was made using both covariance as well as correlation matrix as input.
Various variable selection procedures have been applied on mustard data obtained from the
department of plant breeding, CCSHAU, Hisar for selection of principal variables
Description
Keywords
Principal Component Analysis, Principal variables, Dimensionality- reduction, Variable selection, Best subset, Covariance matrix, Correlation matrix