Описание: The book starts with the quote of the classical Pearson definition of PCA and includes reviews of various methods: NLPCA, ICA, MDS, embedding and clustering algorithms, principal manifolds and SOM. New approaches to NLPCA, principal manifolds, branching principal components and topology preserving mappings are described.
Автор: Steffen L. Lauritzen Название: Extremal Families and Systems of Sufficient Statistics ISBN: 0387968725 ISBN-13(EAN): 9780387968728 Издательство: Springer Рейтинг: Цена: 16769.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The pOint of view behind the present work is that the connection between a statistical model and a statistical analysis-is a dua- lity (in a vague sense).
Автор: Garson, G. David (north Carolina State University, Raleigh, Usa) Название: Factor analysis and dimension reduction in r ISBN: 1032246693 ISBN-13(EAN): 9781032246697 Издательство: Taylor&Francis Рейтинг: Цена: 11023.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Factor Analysis and Dimension Reduction in R provides coverage, with worked examples, of a large number of dimension reduction procedures along with model performance metrics to compare them. Factor analysis in the form of principal components analysis (PCA) or principal factor analysis (PFA) is familiar to most social scientists. However, what is less familiar is understanding that factor analysis is a subset of the more general statistical family of dimension reduction methods.The social scientist's toolkit for factor analysis problems can be expanded to include the range of solutions this book presents. In addition to covering FA and PCA with orthogonal and oblique rotation, this book’s coverage includes higher-order factor models, bifactor models, models based on binary and ordinal data, models based on mixed data, generalized low-rank models, cluster analysis with GLRM, models involving supplemental variables or observations, Bayesian factor analysis, regularized factor analysis, testing for unidimensionality, and prediction with factor scores. The second half of the book deals with other procedures for dimension reduction. These include coverage of kernel PCA, factor analysis with multidimensional scaling, locally linear embedding models, Laplacian eigenmaps, diffusion maps, force directed methods, t-distributed stochastic neighbor embedding, independent component analysis (ICA), dimensionality reduction via regression (DRR), non-negative matrix factorization (NNMF), Isomap, Autoencoder, uniform manifold approximation and projection (UMAP) models, neural network models, and longitudinal factor analysis models. In addition, a special chapter covers metrics for comparing model performance.Features of this book include:Numerous worked examples with replicable R codeExplicit comprehensive coverage of data assumptionsAdaptation of factor methods to binary, ordinal, and categorical dataRe
Автор: Bolla, Marianna Название: Multidimensional Stationary Time Series ISBN: 0367619709 ISBN-13(EAN): 9780367619701 Издательство: Taylor&Francis Рейтинг: Цена: 7042.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Автор: Bolla, Marianna (budapest University Of Technology And Economics) Szabados, Tamas (budapest University Of Technology And Economics, Hungary) Название: Multidimensional stationary time series ISBN: 0367569329 ISBN-13(EAN): 9780367569327 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book gives a brief survey of the theory of multidimensional (multivariate), weakly stationary time series, with emphasis on dimension reduction and prediction.
Автор: Garson, G. David Название: Factor Analysis and Dimension Reduction in R ISBN: 1032246685 ISBN-13(EAN): 9781032246680 Издательство: Taylor&Francis Рейтинг: Цена: 19906.00 р. Наличие на складе: Нет в наличии.
ООО "Логосфера " Тел:+7(495) 980-12-10 www.logobook.ru
