Download or read book Correlated Bayesian Factor Analysis written by Daniel Bryant Rowe and published by . This book was released on 1998 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Factor Analysis written by Teije Jan Euverman and published by . This book was released on 1983 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modern Psychometrics with R written by Patrick Mair and published by Springer. This book was released on 2018-09-20 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook describes the broadening methodology spectrum of psychological measurement in order to meet the statistical needs of a modern psychologist. The way statistics is used, and maybe even perceived, in psychology has drastically changed over the last few years; computationally as well as methodologically. R has taken the field of psychology by storm, to the point that it can now safely be considered the lingua franca for statistical data analysis in psychology. The goal of this book is to give the reader a starting point when analyzing data using a particular method, including advanced versions, and to hopefully motivate him or her to delve deeper into additional literature on the method. Beginning with one of the oldest psychometric model formulations, the true score model, Mair devotes the early chapters to exploring confirmatory factor analysis, modern test theory, and a sequence of multivariate exploratory method. Subsequent chapters present special techniques useful for modern psychological applications including correlation networks, sophisticated parametric clustering techniques, longitudinal measurements on a single participant, and functional magnetic resonance imaging (fMRI) data. In addition to using real-life data sets to demonstrate each method, the book also reports each method in three parts-- first describing when and why to apply it, then how to compute the method in R, and finally how to present, visualize, and interpret the results. Requiring a basic knowledge of statistical methods and R software, but written in a casual tone, this text is ideal for graduate students in psychology. Relevant courses include methods of scaling, latent variable modeling, psychometrics for graduate students in Psychology, and multivariate methods in the social sciences.
Download or read book A Bayesian Approach to Factor Analysis Via Comparing Prior and Posterior Concentration written by Yun Cao and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a factor analysis model that arises as some distribution form known up to first and second moments. We propose a new Bayesian approach to determine if any latent factors exist and the number of factors. As opposed to current Bayesian methodology for factor analysis, our approach only requires the specification of a prior for the mean vector and the variance matrix for the manifest variables. We compare the concentration of the prior and posterior about the various subsets of parameter space specified by the hypothesized factor structures. We consider two priors here, one is conjugate type and the other is based on the correlation factorization of the covariance matrix. A computational problem associated with the use of the second prior is solved by the use of importance sampling for the posterior analysis. If the data does not lead to a substantial increase in the concentration about the relevant subset, of the posterior compared to the prior, then we have evidence against the hypothesized factor structure. The hypothesis is assessed by computing the observed relative surprise. This results in a considerable simplification of the problem, especially with respect to the elicitation of the prior.
Download or read book Assessing Measurement Invariance for Applied Research written by Craig S. Wells and published by Cambridge University Press. This book was released on 2021-06-03 with total page 417 pages. Available in PDF, EPUB and Kindle. Book excerpt: This user-friendly guide illustrates how to assess measurement invariance using computer programs, statistical methods, and real data.
Download or read book Applied Multivariate Analysis written by S. James Press and published by Courier Corporation. This book was released on 2012-09-05 with total page 706 pages. Available in PDF, EPUB and Kindle. Book excerpt: Geared toward upper-level undergraduates and graduate students, this two-part treatment deals with the foundations of multivariate analysis as well as related models and applications. Starting with a look at practical elements of matrix theory, the text proceeds to discussions of continuous multivariate distributions, the normal distribution, and Bayesian inference; multivariate large sample distributions and approximations; the Wishart and other continuous multivariate distributions; and basic multivariate statistics in the normal distribution. The second half of the text moves from defining the basics to explaining models. Topics include regression and the analysis of variance; principal components; factor analysis and latent structure analysis; canonical correlations; stable portfolio analysis; classifications and discrimination models; control in the multivariate linear model; and structuring multivariate populations, with particular focus on multidimensional scaling and clustering. In addition to its value to professional statisticians, this volume may also prove helpful to teachers and researchers in those areas of behavioral and social sciences where multivariate statistics is heavily applied. This new edition features an appendix of answers to the exercises.
Download or read book A Bayesian Approach to Factor Analysis Via Comparing Prior and Posterior Concentration written by and published by . This book was released on 2006 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a factor analysis model that arises as some distribution form known up to first and second moments. We propose a new Bayesian approach to determine if any latent factors exist and the number of factors. As opposed to current Bayesian methodology for factor analysis, our approach only requires the specification of a prior for the mean vector and the variance matrix for the manifest variables. We compare the concentration of the prior and posterior about the various subsets of parameter space specified by the hypothesized factor structures. We consider two priors here, one is conjugate type and the other is based on the correlation factorization of the covariance matrix. A computational problem associated with the use of the second prior is solved by the use of importance sampling for the posterior analysis. If the data does not lead to a substantial increase in the concentration about the relevant subset, of the posterior compared to the prior, then we have evidence against the hypothesized factor structure. The hypothesis is assessed by computing the observed relative surprise. This results in a considerable simplification of the problem, especially with respect to the elicitation of the prior.
Download or read book Factor Analysis written by Richard L. Gorsuch and published by Psychology Press. This book was released on 2013-05-13 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Comprehensive and comprehensible, this classic covers the basic and advanced topics essential for using factor analysis as a scientific tool in psychology, education, sociology, and related areas. Emphasizing the usefulness of the techniques, it presents sufficient mathematical background for understanding and sufficient discussion of applications for effective use. This includes not only theory but also the empirical evaluations of the importance of mathematical distinctions for applied scientific analysis.
Download or read book Factor Analysis and Related Methods written by Roderick P. McDonald and published by Psychology Press. This book was released on 1985 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: First Published in 1985. Routledge is an imprint of Taylor & Francis, an informa company.
Download or read book Bayesian Analysis of a Binary Choice Multidimensional Scaling Model with Correlated Errors Using the Gibbs Sampling Method written by Youngchan Kim and published by . This book was released on 1995 with total page 266 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Bayesian Inference for Gene Expression and Proteomics written by Kim-Anh Do and published by Cambridge University Press. This book was released on 2006-07-24 with total page 437 pages. Available in PDF, EPUB and Kindle. Book excerpt: Expert overviews of Bayesian methodology, tools and software for multi-platform high-throughput experimentation.
Download or read book Multivariate Bayesian Statistics written by Daniel B. Rowe and published by CRC Press. This book was released on 2002-11-25 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Of the two primary approaches to the classic source separation problem, only one does not impose potentially unreasonable model and likelihood constraints: the Bayesian statistical approach. Bayesian methods incorporate the available information regarding the model parameters and not only allow estimation of the sources and mixing coefficients, but
Download or read book Subjective and Objective Bayesian Statistics written by S. James Press and published by John Wiley & Sons. This book was released on 2009-09-25 with total page 591 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ein Wiley-Klassiker über Bayes-Statistik, jetzt in durchgesehener und erweiterter Neuauflage! - Werk spiegelt die stürmische Entwicklung dieses Gebietes innerhalb der letzten Jahre wider - vollständige Darstellung der theoretischen Grundlagen - jetzt ergänzt durch unzählige Anwendungsbeispiele - die wichtigsten modernen Methoden (u. a. hierarchische Modellierung, linear-dynamische Modellierung, Metaanalyse, MCMC-Simulationen) - einzigartige Diskussion der Finetti-Transformierten und anderer Themen, über die man ansonsten nur spärliche Informationen findet - Lösungen zu den Übungsaufgaben sind enthalten
Download or read book Bayesian Reasoning in Data Analysis written by Giulio D'Agostini and published by World Scientific. This book was released on 2003 with total page 351 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a multi-level introduction to Bayesian reasoning (as opposed to OC conventional statisticsOCO) and its applications to data analysis. The basic ideas of this OC newOCO approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and are shown often to coincide OCo under well-defined assumptions! OCo with OC standardOCO methods, which can therefore be seen as special cases of the more general Bayesian methods. In dealing with uncertainty in measurements, modern metrological ideas are utilized, including the ISO classification of uncertainty into type A and type B. These are shown to fit well into the Bayesian framework.
Download or read book A Bayesian Family Factor Model for Multiple Outcomes written by Qiaolin Chen and published by . This book was released on 2014 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt: The UCLA Neurocognitive Family Study (NFS) collected multiple measurements on schizophrenia (SZ) patients and their relatives, as well as control subjects and their relatives, to study heritable vulnerability factors for schizophrenia. Each family has several members enrolled in the study and the same multiple outcomes were measured on each person. The relationship structure is complicated because not only observations on individuals from the same family are correlated, but the multiple outcome measures on the same individuals are also correlated. Traditional familial data analyses model outcomes separately and thus do not provide information about the interrelationships among them. I propose a Bayesian Family Factor Model (BFFM), which extends the classical confirmatory factor analysis (CFA) model to explain the correlations among observed variables using a combination of family-member factors and outcome factors. Traditional methods for fitting CFA models, such as full information maximum likelihood (FIML) estimation using quasi-Newton optimization (QNO) can have convergence problems and Heywood cases caused by empirical under-identification. In contrast, modern Bayesian Markov chain Monte Carlo (MCMC) handles these inference problems easily. Simulations compare the BFFM to FIML-QNO in settings where the true covariance matrix is identified, close to not identified and not identified. For these settings, FIML-QNO fails to fit the data in 85%, 57% and 13% of the cases, respectively, due to non-convergence or invalid estimates, while MCMC provides stable estimates. When both methods successfully fit the data, estimates from the BFFM have smaller variances and comparable mean squared errors. BFFM can test hypotheses of interest easily using Bayes factors computed as the Savage-Dickey ratios. I illustrate the BFFM by analyzing the UCLA NFS data and test hypotheses about differences in means between SZ and control families. Tests of the group mean differences using posterior probabilities suggest that SZ probands perform worse in all 17 neurocogitive measures than control probands, while mothers of SZ subjects do worse than control mothers.
Download or read book Frontiers of Statistical Decision Making and Bayesian Analysis written by Ming-Hui Chen and published by Springer Science & Business Media. This book was released on 2010-07-24 with total page 631 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. This book provides a review of current research challenges and opportunities. While the book can not exhaustively cover all current research areas, it does include some exemplary discussion of most research frontiers. Topics include objective Bayesian inference, shrinkage estimation and other decision based estimation, model selection and testing, nonparametric Bayes, the interface of Bayesian and frequentist inference, data mining and machine learning, methods for categorical and spatio-temporal data analysis and posterior simulation methods. Several major application areas are covered: computer models, Bayesian clinical trial design, epidemiology, phylogenetics, bioinformatics, climate modeling and applications in political science, finance and marketing. As a review of current research in Bayesian analysis the book presents a balance between theory and applications. The lack of a clear demarcation between theoretical and applied research is a reflection of the highly interdisciplinary and often applied nature of research in Bayesian statistics. The book is intended as an update for researchers in Bayesian statistics, including non-statisticians who make use of Bayesian inference to address substantive research questions in other fields. It would also be useful for graduate students and research scholars in statistics or biostatistics who wish to acquaint themselves with current research frontiers.
Download or read book Bayesian Structural Equation Modeling written by Sarah Depaoli and published by Guilford Publications. This book was released on 2021-08-16 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers researchers a systematic and accessible introduction to using a Bayesian framework in structural equation modeling (SEM). Stand-alone chapters on each SEM model clearly explain the Bayesian form of the model and walk the reader through implementation. Engaging worked-through examples from diverse social science subfields illustrate the various modeling techniques, highlighting statistical or estimation problems that are likely to arise and describing potential solutions. For each model, instructions are provided for writing up findings for publication, including annotated sample data analysis plans and results sections. Other user-friendly features in every chapter include "Major Take-Home Points," notation glossaries, annotated suggestions for further reading, and sample code in both Mplus and R. The companion website (www.guilford.com/depaoli-materials) supplies data sets; annotated code for implementation in both Mplus and R, so that users can work within their preferred platform; and output for all of the book’s examples.
