Statistical factor analysis and related methods : theory and applications
Author(s)
Bibliographic Information
Statistical factor analysis and related methods : theory and applications
(Wiley series in probability and mathematical statistics, . Probability and mathematical statistics)
Wiley, c1994
Available at 54 libraries
  Aomori
  Iwate
  Miyagi
  Akita
  Yamagata
  Fukushima
  Ibaraki
  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
  Kanagawa
  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
  Shiga
  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
  Thailand
  United Kingdom
  Germany
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  France
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  United States of America
Note
"A Wiley-Interscience publication"
Includes bibliographical references (p. 690-731) and index
Description and Table of Contents
Description
Statistical Factor Analysis and Related Methods Theory andApplications In bridging the gap between the mathematical andstatistical theory of factor analysis, this new work represents thefirst unified treatment of the theory and practice of factoranalysis and latent variable models. It focuses on such areasas:
* The classical principal components model and sample-populationinference
* Several extensions and modifications of principal components,including Q and three-mode analysis and principal components in thecomplex domain
* Maximum likelihood and weighted factor models, factoridentification, factor rotation, and the estimation of factorscores
* The use of factor models in conjunction with various types ofdata including time series, spatial data, rank orders, and nominalvariable
* Applications of factor models to the estimation of functionalforms and to least squares of regression estimators
Table of Contents
Preliminaries.
Matrixes, Vector Spaces.
The Ordinary Principal Components Model.
Statistical Testing of the Ordinary Principal ComponentsModel.
Extensions of the Ordinary Principal Components Model.
Factor Analysis.
Factor Analysis of Correlated Observations.
Ordinal and Nominal Random Data.
Other Models for Discrete Data.
Factor Analysis and Least Squares Regression.
Exercises.
References.
Index.
by "Nielsen BookData"