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## Details

Genre/Form: | Electronic books |
---|---|

Additional Physical Format: | Print version: Jolliffe, I.T. Principal component analysis. New York : Springer-Verlag, ©1986 (DLC) 85027882 (OCoLC)13673106 |

Material Type: | Document, Internet resource |

Document Type: | Internet Resource, Computer File |

All Authors / Contributors: |
I T Jolliffe |

ISBN: | 9781475719048 1475719043 9781475719062 147571906X |

OCLC Number: | 760965654 |

Description: | 1 online resource (xiii, 271 pages) : illustrations |

Contents: | 1 Introduction -- 2 Mathematical and Statistical Properties of Population Principal Components -- 3 Mathematical and Statistical Properties of Sample Principal Components -- 4 Principal Components as a Small Number of Interpretable Variables: Some Examples -- 5 Graphical Representation of Data Using Principal Components -- 6 Choosing a Subset of Principal Components or Variables -- 7 Principal Component Analysis and Factor Analysis -- 8 Principal Components in Regression Analysis -- 9 Principal Components Used with Other Multivariate Techniques -- 10 Outlier Detection, Influential Observations and Robust Estimation of Principal Components -- 11 Principal Component Analysis for Special Types of Data -- 12 Generalizations and Adaptations of Principal Component Analysis -- Computation of Principal Components -- A1. Numerical Calculation of Principal Components -- A2. Principal Component Analysis in Computer Packages -- References. |

Series Title: | Springer series in statistics. |

Responsibility: | I.T. Jolliffe. |

More information: |

### Abstract:

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, but it is now weIl entrenched in virtually every statistical computer package. The central idea of principal component analysis is to reduce the dimen sionality of a data set in which there are a large number of interrelated variables, while retaining as much as possible of the variation present in the data set. This reduction is achieved by transforming to a new set of variables, the principal components, which are uncorrelated, and which are ordered so that the first few retain most of the variation present in all of the original variables. Computation of the principal components reduces to the solution of an eigenvalue-eigenvector problem for a positive-semidefinite symmetrie matrix. Thus, the definition and computation of principal components are straightforward but, as will be seen, this apparently simple technique has a wide variety of different applications, as weIl as a number of different deri vations. Any feelings that principal component analysis is a narrow subject should soon be dispelled by the present book; indeed some quite broad topics which are related to principal component analysis receive no more than a brief mention in the final two chapters.

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