summary:The author shows that a decomposition of a covariance matrix $\bold{\sum = AA'}$ implies the corresponding model, i.e. the existence of factors $f_j$ such that $\sum a_{ij}f_j$ is true. The result is applied to the general linear model of factor analysis. A procedure for computing the factor score is proposed
A comparison between Principal Component Analysis (PCA) and Factor Analysis (FA) is performed both t...
summary:An estimation of the linear function of elements of unknown matrices in the covariance compo...
Introduction Factor Analysis and Structural Theories Brief History of Factor Analysis as a Linear Mo...
summary:The author shows that a decomposition of a covariance matrix $\bold{\sum = AA'}$ implies the...
Un dels resultats principals de l'anàalisi factorial afirma que si el model factorial sesatis...
A new factor analysis (FA) procedure has recently been proposed which can be called matrix decomposi...
Factor models are a very efficient way to describe high-dimensional vectors of data in terms of a sm...
The classical fitting problem in exploratory factor analysis (EFA) is to find estimates for the fact...
Factor analysis is a statistical technique, the aim of which is to simplify a complex data set by re...
This paper gives a generalization of results presented by ten Berge, Krijnen, Wansbeek & Shapiro. Th...
TEZ5456Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2005.Kaynakça (s. 116-120) var.xiii, 145 s. ; ...
Factor analysis is often applied in empirical data analysis to explore data structures. Due to its t...
Factor models of security returns decompose the random return on each of a cross-section of assets i...
We report a matrix expression for the covariance matrix of MLEs of factor loadings in factor analysi...
Factor analysis aims to describe high dimensional random vectors by means of a small number of unkno...
A comparison between Principal Component Analysis (PCA) and Factor Analysis (FA) is performed both t...
summary:An estimation of the linear function of elements of unknown matrices in the covariance compo...
Introduction Factor Analysis and Structural Theories Brief History of Factor Analysis as a Linear Mo...
summary:The author shows that a decomposition of a covariance matrix $\bold{\sum = AA'}$ implies the...
Un dels resultats principals de l'anàalisi factorial afirma que si el model factorial sesatis...
A new factor analysis (FA) procedure has recently been proposed which can be called matrix decomposi...
Factor models are a very efficient way to describe high-dimensional vectors of data in terms of a sm...
The classical fitting problem in exploratory factor analysis (EFA) is to find estimates for the fact...
Factor analysis is a statistical technique, the aim of which is to simplify a complex data set by re...
This paper gives a generalization of results presented by ten Berge, Krijnen, Wansbeek & Shapiro. Th...
TEZ5456Tez (Doktora) -- Çukurova Üniversitesi, Adana, 2005.Kaynakça (s. 116-120) var.xiii, 145 s. ; ...
Factor analysis is often applied in empirical data analysis to explore data structures. Due to its t...
Factor models of security returns decompose the random return on each of a cross-section of assets i...
We report a matrix expression for the covariance matrix of MLEs of factor loadings in factor analysi...
Factor analysis aims to describe high dimensional random vectors by means of a small number of unkno...
A comparison between Principal Component Analysis (PCA) and Factor Analysis (FA) is performed both t...
summary:An estimation of the linear function of elements of unknown matrices in the covariance compo...
Introduction Factor Analysis and Structural Theories Brief History of Factor Analysis as a Linear Mo...
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