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Principal Component Analysis Is Central To The Study Of Multivariate Data. Although One Of The Earliest Multivariate Techniques, It Continues To Be The Subject Of Much Research, Ranging From New Model-based Approaches To Algorithmic Ideas From Neural Networks. It Is Extremely Versatile, With Applications In Many Disciplines. The First Edition Of This Book Was The First Comprehensive Text Written Solely On Principal Component Analysis. The Second Edition Updates And Substantially Expands The Original Version, And Is Once Again The Definitive Text On The Subject. It Includes Core Material, Current Research And A Wide Range Of Applications. Its Length Is Nearly Double That Of The First Edition. Researchers In Statistics, Or In Other Fields That Use Principal Component Analysis, Will Find That The Book Gives An Authoritative Yet Accessible Account Of The Subject. It Is Also A Valuable Resource For Graduate Courses In Multivariate Analysis. The Book Requires Some Knowledge Of Matrix Algebra. Ian Jolliffe Is Professor Of Statistics At The University Of Aberdeen. He Is Author Or Co-author Of Over 60 Research Papers And Three Other Books. His Research Interests Are Broad, But Aspects Of Principal Component Analysis Have Fascinated Him And Kept Him Busy For Over 30 Years. Introduction -- Properties Of Population Principal Components -- Properties Of Sample Principal Components -- Interpreting Principal Components: Examples -- Graphical Representation Of Data Using Principal Components -- Choosing A Subset Of Principal Components Or Variables -- Principal Component Analysis And Factor Analysis -- Principal Components In Regression Analysis -- Principal Components Used With Other Multivariate Techniques -- Outlier Detection, Influential Observations And Robust Estimation -- Rotation And Interpretation Of Principal Components -- Principal Component Analysis For Time Series And Other Non-independent Data -- Principal Component Analysis For Special Types Of Data -- Generalizations And Adaptations Of Principal Component Analysis. I.t. Jolliffe. Includes Bibliographical References (p. [415]-457) And Indexes.
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Principal Component Analysis Is Probably The Oldest And Best Known Of The It Was First Introduced By Pearson (1901), Techniques Ofmultivariate Analysis. And Developed Independently By Hotelling (1933). Like Many Multivariate Methods, It Was Not Widely Used Until The Advent Of Electronic Computers, B
Principal Component Analysis Is Central To The Study Of Multivariate Data. Although One Of The Earliest Multivariate Techniques, It Continues To Be The Subject Of Much Research, Ranging From New Model-based Approaches To Algorithmic Ideas From Neural Networks. It Is Extremely Versatile, With Applica
## Abstract Principal component analysis (PCA) is a multivariate technique that analyzes a data table in which observations are described by several interβcorrelated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new or
The principal components of a vector of random variables are related to the common factors of a factor analysis model for this vector. Conditions are presented under which components and factors as well as factor proxies come close to each other. A similar analysis is carried out for the matrices of