Results 261 to 270 of about 6,103,116 (298)
Some of the next articles are maybe not open access.
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.
Kaiser, Henry F., Caffrey, J.
openaire +3 more sources
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.
Kaiser, Henry F., Caffrey, J.
openaire +3 more sources
Fisher Discriminant Analysis and Factor Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence, 1983We show that information on the inherent structure of multidimensional data derived from a factor analysis procedure is equivalent to information obtained by Fisher discriminant analysis techniques, provided certain conditions, usually required in the factor analysis model, are satisfied.
Giacomo Della Riccia +1 more
openaire +3 more sources
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.
Lawley, D. N., Maxwell, A. E.
openaire +2 more sources
Journal of Chemical Information and Computer Sciences, 1979
Abstract : Principal components and other standard factor analyses can yield misleading results if an assumed subsidiary condition is untrue or if data are missing. Principal components factor analysis is tested for its reliability, using a problem with known answers.
C. Gardner Swain +2 more
openaire +1 more source
Abstract : Principal components and other standard factor analyses can yield misleading results if an assumed subsidiary condition is untrue or if data are missing. Principal components factor analysis is tested for its reliability, using a problem with known answers.
C. Gardner Swain +2 more
openaire +1 more source
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 ...
openaire +2 more sources
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 ...
openaire +2 more sources
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.
openaire +2 more sources
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.
openaire +2 more sources
FACTOR INTERACTION IN NONLINEAR FACTOR ANALYSIS
British Journal of Mathematical and Statistical Psychology, 1967Factor interaction models are defined as those cases in nonlinear factor analysis in which the specification equation contains products of two or more latent variables or functions of latent variables. A complete algebraic treatment is given for the case of a product of two latent variables, and certain more general cases are briefly outlined.
openaire +2 more sources
Heteroscedastic factor analysis
Biometrika, 2003zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Lewin-Koh, S.-C., Amemiya, Y.
openaire +2 more sources