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Yoshio Takane has written 4 work(s)

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Product Description: Winner of the 2015 Sugiyama Meiko Award (Publication Award) of the Behaviormetric Society of Japan Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling...read more

Hardcover:

9781466592940 | Chapman & Hall, December 11, 2014, cover price $99.95 |

*About this edition:*Winner of the 2015 Sugiyama Meiko Award (Publication Award) of the Behaviormetric Society of Japan Developed by the authors, generalized structured component analysis is an alternative to two longstanding approaches to structural equation modeling: covariance structure analysis and partial least squares path modeling.Product Description: Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis. The former underlies the least squares estimation in regression analysis, which is essentially a projection of one subspace onto another, and the latter underlies principal component analysis, which seeks to find a subspace that captures the largest variability in the original space...read more

Hardcover:

9781441998866 | Springer Verlag, April 1, 2011, cover price $129.00 |

*About this edition:*Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis.Paperback:

9781461428596 | Springer Verlag, May 29, 2013, cover price $129.00 |

*About this edition:*Aside from distribution theory, projections and the singular value decomposition (SVD) are the two most important concepts for understanding the basic mechanism of multivariate analysis.Product Description: In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data. How can regression analysis and PCA be combined in a beneficial way? Why and when is it a good idea to combine them? What kind of benefits are we getting from them? Addressing these questions, Constrained Principal Component Analysis and Related Techniques shows how constrained PCA (CPCA) offers a unified framework for these approaches...read more

Hardcover:

9781466556669 | Chapman & Hall, October 24, 2013, cover price $94.95 |

*About this edition:*In multivariate data analysis, regression techniques predict one set of variables from another while principal component analysis (PCA) finds a subspace of minimal dimensionality that captures the largest variability in the data.Hardcover:

9780070204850 | 6 sub edition (McGraw-Hill College, February 1, 1989), cover price $125.00 |

*About this edition:*This is a main text for an upper-level undergraduate or graduate-level introductory statistics course in departments of psychology, educational psychology, education and related areas.
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