Principal variables selection in multivariate analysis

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dc.contributor.advisorHooda, B.K.
dc.contributor.authorDeepti Singh
dc.date.accessioned2016-11-21T14:00:02Z
dc.date.available2016-11-21T14:00:02Z
dc.date.issued2007
dc.description.abstractPractical 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 variablesen_US
dc.identifier.urihttp://krishikosh.egranth.ac.in/handle/1/86664
dc.language.isoenen_US
dc.publisherCCSHAUen_US
dc.subStatistics
dc.subjectPrincipal Component Analysis, Principal variables, Dimensionality- reduction, Variable selection, Best subset, Covariance matrix, Correlation matrixen_US
dc.these.typeM.Sc
dc.titlePrincipal variables selection in multivariate analysisen_US
dc.typeThesisen_US
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