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बेयसियन रिग्रेशन×पुष्टिकारीय कारक विश्लेषण (CFA)×अन्वेषणात्मक कारक विश्लेषण (EFA)×
क्षेत्रबायेसियनसांख्यिकीसांख्यिकी
परिवारBayesian methodsLatent structureLatent structure
उद्भव वर्ष1969
प्रवर्तकKarl Jöreskog
प्रकारBayesian linear modelConfirmatory latent variable modelLatent variable / dimension reduction
मौलिक स्रोतGelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A. & Rubin, D. B. (2013). Bayesian Data Analysis (3rd ed.). CRC Press. ISBN: 978-1439840955Brown, T. A. (2015). Confirmatory Factor Analysis for Applied Research (2nd ed.). The Guilford Press. ISBN: 978-1462515363Fabrigar, L. R., Wegener, D. T., MacCallum, R. C. & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. DOI ↗
उपनामbayesian linear regression, probabilistic regression, bayesian regresyonDoğrulayıcı Faktör Analizi (CFA), confirmatory factor analysis, measurement modelcommon factor analysis, açımlayıcı faktör analizi, factor analysis
संबंधित244
सारांशBayesian regression is a probabilistic version of linear regression that treats the model parameters as uncertain quantities. Instead of returning a single best-fit estimate, it combines prior knowledge with the observed data to produce a full posterior probability distribution for each parameter, from which credible intervals and predictions are read off.Confirmatory factor analysis tests whether a researcher-specified factor structure fits the observed data. Formalised by Karl Jöreskog in 1969, it is the measurement-model step within structural equation modelling and is the standard tool for validating the factorial structure of scales and questionnaires before comparing groups or estimating latent relationships.Exploratory factor analysis reduces a large set of observed variables into a smaller number of latent common factors. It is widely used in scale development and psychometrics to uncover the dimensional structure that underlies a set of correlated items, without specifying that structure in advance.
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