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Factor analysis regression [PDF]
Statistical Papers, 2006 In the presence of multicollinearity the literature points to principal component regression (PCR) as an estimation method for the regression coefficients of a multiple regression model. Due to ambiguities in the interpretation, involved by the orthogonal transformation of the set of explanatory variables, the method could not yet gain wide acceptance.
Kosfeld, Reinhold +1 more
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Psychometrika, 1965
A distinction is made between statistical inference and psychometric inference in factor analysis. After reviewing Rao's canonical factor analysis (CFA), a fundamental statistical method of factoring, a new method of factor analysis based upon the psychometric concept of generalizability is described.
Henry F. Kaiser, John Caffrey
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A distinction is made between statistical inference and psychometric inference in factor analysis. After reviewing Rao's canonical factor analysis (CFA), a fundamental statistical method of factoring, a new method of factor analysis based upon the psychometric concept of generalizability is described.
Henry F. Kaiser, John Caffrey
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Heteroscedastic factor analysis
Biometrika, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lewin-Koh, S.-C., Amemiya, Y.
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Analytical Chemistry, 2001
Bilinear data matrices may be resolved into abstract factors by factor analysis. The underlying chemical processes that generated the data may be deduced from the abstract factors by hard (model fitting) or soft (model-free) analyses. We propose a novel approach that combines the advantages of both hard and soft methods, in that only a few parameters ...
Mason, Caroline +2 more
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Bilinear data matrices may be resolved into abstract factors by factor analysis. The underlying chemical processes that generated the data may be deduced from the abstract factors by hard (model fitting) or soft (model-free) analyses. We propose a novel approach that combines the advantages of both hard and soft methods, in that only a few parameters ...
Mason, Caroline +2 more
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Regression and Factor Analysis
Biometrika, 1973SUMMARY A basic model of factor analysis is employed in the estimation of multiple correlation coefficients and partial regression weights. Estimators are derived for situations in which some or all of the independent variates are subject to errors in measurement.
A. E. Maxwell, D. N. Lawley
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The foundations of factor analysis
Biometrika, 1984A new approach to factor analysis and related latent variable methods is proposed which is based on data reduction using the idea of Bayesian sufficiency. Considerations of symmetry, invariance and independence are used to determine an appropriate family of models.
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British Journal of Mathematical and Statistical Psychology, 1970
It is shown that PFA, CFA and AFA are particular cases of a scale‐invariant factoring procedure based on variance ratios of certain weighted combinations of variables. Standard derivations in the literature are shown, in contrast, to have unsatisfactory features.
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It is shown that PFA, CFA and AFA are particular cases of a scale‐invariant factoring procedure based on variance ratios of certain weighted combinations of variables. Standard derivations in the literature are shown, in contrast, to have unsatisfactory features.
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Neural Computation, 1999
We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and unifies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square noiseless mixing but also the general case where the ...
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We introduce the independent factor analysis (IFA) method for recovering independent hidden sources from their observed mixtures. IFA generalizes and unifies ordinary factor analysis (FA), principal component analysis (PCA), and independent component analysis (ICA), and can handle not only square noiseless mixing but also the general case where the ...
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Psychometrika, 1987
The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the order determination of an autoregressive model to the determination of the number of factors in the maximum likelihood factor analysis.
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The information criterion AIC was introduced to extend the method of maximum likelihood to the multimodel situation. It was obtained by relating the successful experience of the order determination of an autoregressive model to the determination of the number of factors in the maximum likelihood factor analysis.
openaire +2 more sources