Principal Component Analysis. 2 ed., I.T. Jolliffe
Старое издание
Автор: Jolliffe I.T. Название: Principal Component Analysis. 2 ed. ISBN: 0387954422 ISBN-13(EAN): 9780387954424 Издательство: Springer Цена: 34937.00 р. Наличие на складе: Есть у поставщикаПоставка под заказ. Описание: 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. Its length is nearly double that of the first edition.
Автор: Jolliffe I.T. Название: Principal Component Analysis. 2 ed. ISBN: 0387954422 ISBN-13(EAN): 9780387954424 Издательство: Springer Рейтинг: Цена: 34937.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: 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. Its length is nearly double that of the first edition.
Автор: T. M. V., Suryanarayana Mistry, P. B. Название: Principal component regression for crop yield estimation ISBN: 9811006628 ISBN-13(EAN): 9789811006623 Издательство: Springer Рейтинг: Цена: 9141.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book highlights the estimation of crop yield in CentralGujarat, especially with regard to the development of Multiple RegressionModels and Principal Component Regression (PCR) models using climatologicalparameters as independent variables and crop yield as a dependent variable.
Описание: This book expounds the principle and related applications of nonlinear principal component analysis (PCA), which is useful method to analyze mixed measurement levels data.
Автор: James D. Malley Название: Optimal Unbiased Estimation of Variance Components ISBN: 0387964495 ISBN-13(EAN): 9780387964492 Издательство: Springer Рейтинг: Цена: 14673.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The clearest way into the Universe is through a forest wilderness. John MuIr As recently as 1970 the problem of obtaining optimal estimates for variance components in a mixed linear model with unbalanced data was considered a miasma of competing, generally weakly motivated estimators, with few firm gUidelines and many simple, compelling but Unanswered questions. Then in 1971 two significant beachheads were secured: the results of Rao 1971a, 1971b] and his MINQUE estimators, and related to these but not originally derived from them, the results of Seely 1971] obtained as part of his introduction of the no ion of quad- ratic subspace into the literature of variance component estimation. These two approaches were ultimately shown to be intimately related by Pukelsheim 1976], who used a linear model for the com- ponents given by Mitra 1970], and in so doing, provided a mathemati- cal framework for estimation which permitted the immediate applica- tion of many of the familiar Gauss-Markov results, methods which had earlier been so successful in the estimation of the parameters in a linear model with only fixed effects. Moreover, this usually enor- mous linear model for the components can be displayed as the starting point for many of the popular variance component estimation tech- niques, thereby unifying the subject in addition to generating answers.
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