Factor Analysis And Principal Component Analysis
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Factor Analysis And Principal Component Analysis
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Author : Di Franco
language : en
Publisher: FrancoAngeli
Release Date : 2013
Factor Analysis And Principal Component Analysis written by Di Franco and has been published by FrancoAngeli this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013 with Social Science categories.
Principal Components Analysis
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Author : George H. Dunteman
language : en
Publisher: SAGE
Release Date : 1989-05
Principal Components Analysis written by George H. Dunteman and has been published by SAGE this book supported file pdf, txt, epub, kindle and other format this book has been release on 1989-05 with Mathematics categories.
For anyone in need of a concise, introductory guide to principal components analysis, this book is a must. Through an effective use of simple mathematical-geometrical and multiple real-life examples (such as crime statistics, indicators of drug abuse, and educational expenditures) -- and by minimizing the use of matrix algebra -- the reader can quickly master and put this technique to immediate use.
Principal Component Analysis
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Author : I.T. Jolliffe
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
Principal Component Analysis written by I.T. Jolliffe and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-03-09 with Mathematics categories.
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.
Factor Analysis And Dimension Reduction In R
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Author : G. David Garson
language : en
Publisher: Taylor & Francis
Release Date : 2022-12-16
Factor Analysis And Dimension Reduction In R written by G. David Garson and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-12-16 with Psychology categories.
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 code Explicit comprehensive coverage of data assumptions Adaptation of factor methods to binary, ordinal, and categorical data Residual and outlier analysis Visualization of factor results Final chapters that treat integration of factor analysis with neural network and time series methods Presented in color with R code and introduction to R and RStudio, this book will be suitable for graduate-level and optional module courses for social scientists, and on quantitative methods and multivariate statistics courses.
Three Mode Principal Component Analysis
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Author : Pieter M. Kroonenberg
language : en
Publisher:
Release Date : 1983
Three Mode Principal Component Analysis written by Pieter M. Kroonenberg and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1983 with Multivariate analysis categories.
Closeness Of Factor Analysis And Principal Component Analysis In Semi High Dimensional Conditions
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Author : Lu Liang
language : en
Publisher:
Release Date : 2016
Closeness Of Factor Analysis And Principal Component Analysis In Semi High Dimensional Conditions written by Lu Liang and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016 with categories.
Illustrative Examples Of Principal Component Analysis Using Spss X Factor
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Author : Walter Theodore Federer
language : en
Publisher:
Release Date : 1987
Illustrative Examples Of Principal Component Analysis Using Spss X Factor written by Walter Theodore Federer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1987 with categories.
In order to provide a deeper understanding of the workings of principal components, four data sets were constructed by taking linear combinations of values of two uncorrelated variables to form the X-variates for the principal component analysis. The examples highlight some of the properties and limitations of principal component analysis. This is part of a continuing project that produces annotated computer output for principal component analysis. The complete project will involve processing four examples on SAS/PRINCOMP, BMDP/4M, SPSS-X/FACTOR, GENSTAT/PCP, and SYSTAT/FACTOR. We show here the results from SPSS-X/FACTOR, Release 2.2.
The Essentials Of Factor Analysis
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Author : Dennis Child
language : en
Publisher: A&C Black
Release Date : 2006-06-23
The Essentials Of Factor Analysis written by Dennis Child and has been published by A&C Black this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-06-23 with Social Science categories.
Advances In Principal Component Analysis
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Author : Fausto Pedro García Márquez
language : en
Publisher: BoD – Books on Demand
Release Date : 2022-08-25
Advances In Principal Component Analysis written by Fausto Pedro García Márquez and has been published by BoD – Books on Demand this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-25 with Computers categories.
This book describes and discusses the use of principal component analysis (PCA) for different types of problems in a variety of disciplines, including engineering, technology, economics, and more. It presents real-world case studies showing how PCA can be applied with other algorithms and methods to solve both large and small and static and dynamic problems. It also examines improvements made to PCA over the years.
Factor Analysis As A Statistical Method
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Author : D. N. Lawley
language : en
Publisher:
Release Date : 1971
Factor Analysis As A Statistical Method written by D. N. Lawley and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1971 with Factor analysis categories.