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David J. Bartholomew Latent Variable Models and Factor Analysis


Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency. This book: Provides a unified approach showing how such apparently diverse methods as Latent Class Analysis and Factor Analysis are actually members of the same family. Presents new material on ordered manifest variables, MCMC methods, non-linear models as well as a new chapter on related techniques for investigating dependency. Includes new sections on structural equation models (SEM) and Markov Chain Monte Carlo methods for parameter estimation, along with new illustrative examples. Looks at recent developments on goodness-of-fit test statistics and on non-linear models and models with mixed latent variables, both categorical and continuous. No prior acquaintance with latent variable modelling is pre-supposed but a broad understanding of statistical theory will make it easier to see the approach in its proper perspective. Applied statisticians, psychometricians, medical statisticians, biostatisticians, economists and social science researchers will benefit from this book.

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Collins Linda M. Latent Class and Latent Transition Analysis. With Applications in the Social, Behavioral, and Health Sciences


A modern, comprehensive treatment of latent class and latent transition analysis for categorical data On a daily basis, researchers in the social, behavioral, and health sciences collect information and fit statistical models to the gathered empirical data with the goal of making significant advances in these fields. In many cases, it can be useful to identify latent, or unobserved, subgroups in a population, where individuals' subgroup membership is inferred from their responses on a set of observed variables. Latent Class and Latent Transition Analysis provides a comprehensive and unified introduction to this topic through one-of-a-kind, step-by-step presentations and coverage of theoretical, technical, and practical issues in categorical latent variable modeling for both cross-sectional and longitudinal data. The book begins with an introduction to latent class and latent transition analysis for categorical data. Subsequent chapters delve into more in-depth material, featuring: A complete treatment of longitudinal latent class models Focused coverage of the conceptual underpinnings of interpretation and evaluationof a latent class solution Use of parameter restrictions and detection of identification problems Advanced topics such as multi-group analysis and the modeling and interpretation of interactions between covariates The authors present the topic in a style that is accessible yet rigorous. Each method is presented with both a theoretical background and the practical information that is useful for any data analyst. Empirical examples showcase the real-world applications of the discussed concepts and models, and each chapter concludes with a «Points to Remember» section that contains a brief summary of key ideas. All of the analyses in the book are performed using Proc LCA and Proc LTA, the authors' own software packages that can be run within the SAS® environment. A related Web site houses information on these freely available programs and the book's data sets, encouraging readers to reproduce the analyses and also try their own variations. Latent Class and Latent Transition Analysis is an excellent book for courses on categorical data analysis and latent variable models at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the social, behavioral, and health sciences who conduct latent class and latent transition analysis in their everyday work.

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Группа авторов Statistical Factor Analysis and Related Methods


Statistical Factor Analysis and Related Methods Theory and Applications In bridging the gap between the mathematical and statistical theory of factor analysis, this new work represents the first unified treatment of the theory and practice of factor analysis and latent variable models. It focuses on such areas as: * The classical principal components model and sample-population inference * Several extensions and modifications of principal components, including Q and three-mode analysis and principal components in the complex domain * Maximum likelihood and weighted factor models, factor identification, factor rotation, and the estimation of factor scores * The use of factor models in conjunction with various types of data including time series, spatial data, rank orders, and nominal variable * Applications of factor models to the estimation of functional forms and to least squares of regression estimators

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Patrick Curran J. Latent Curve Models


An effective technique for data analysis in the social sciences The recent explosion in longitudinal data in the social sciences highlights the need for this timely publication. Latent Curve Models: A Structural Equation Perspective provides an effective technique to analyze latent curve models (LCMs). This type of data features random intercepts and slopes that permit each case in a sample to have a different trajectory over time. Furthermore, researchers can include variables to predict the parameters governing these trajectories. The authors synthesize a vast amount of research and findings and, at the same time, provide original results. The book analyzes LCMs from the perspective of structural equation models (SEMs) with latent variables. While the authors discuss simple regression-based procedures that are useful in the early stages of LCMs, most of the presentation uses SEMs as a driving tool. This cutting-edge work includes some of the authors' recent work on the autoregressive latent trajectory model, suggests new models for method factors in multiple indicators, discusses repeated latent variable models, and establishes the identification of a variety of LCMs. This text has been thoroughly class-tested and makes extensive use of pedagogical tools to aid readers in mastering and applying LCMs quickly and easily to their own data sets. Key features include: Chapter introductions and summaries that provide a quick overview of highlights Empirical examples provided throughout that allow readers to test their newly found knowledge and discover practical applications Conclusions at the end of each chapter that stress the essential points that readers need to understand for advancement to more sophisticated topics Extensive footnoting that points the way to the primary literature for more information on particular topics With its emphasis on modeling and the use of numerous examples, this is an excellent book for graduate courses in latent trajectory models as well as a supplemental text for courses in structural modeling. This book is an excellent aid and reference for researchers in quantitative social and behavioral sciences who need to analyze longitudinal data.

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Kamel Gana Structural Equation Modeling with lavaan


This book presents an introduction to structural equation modeling (SEM) and facilitates the access of students and researchers in various scientific fields to this powerful statistical tool. It offers a didactic initiation to SEM as well as to the open-source software, lavaan, and the rich and comprehensive technical features it offers. Structural Equation Modeling with lavaan thus helps the reader to gain autonomy in the use of SEM to test path models and dyadic models, perform confirmatory factor analyses and estimate more complex models such as general structural models with latent variables and latent growth models. SEM is approached both from the point of view of its process (i.e. the different stages of its use) and from the point of view of its product (i.e. the results it generates and their reading).

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Jichuan Wang Structural Equation Modeling


Presents a useful guide for applications of SEM whilst systematically demonstrating various SEM models using M plus Focusing on the conceptual and practical aspects of Structural Equation Modeling (SEM), this book demonstrates basic concepts and examples of various SEM models, along with updates on many advanced methods, including confirmatory factor analysis (CFA) with categorical items, bifactor model, Bayesian CFA model, item response theory (IRT) model, graded response model (GRM), multiple imputation (MI) of missing values, plausible values of latent variables, moderated mediation model, Bayesian SEM, latent growth modeling (LGM) with individually varying times of observations, dynamic structural equation modeling (DSEM), residual dynamic structural equation modeling (RDSEM), testing measurement invariance of instrument with categorical variables, longitudinal latent class analysis (LLCA), latent transition analysis (LTA), growth mixture modeling (GMM) with covariates and distal outcome, manual implementation of the BCH method and the three-step method for mixture modeling, Monte Carlo simulation power analysis for various SEM models, and estimate sample size for latent class analysis (LCA) model. The statistical modeling program Mplus Version 8. 2 is featured with all models updated. It provides researchers with a flexible tool that allows them to analyze data with an easy-to-use interface and graphical displays of data and analysis results. Intended as both a teaching resource and a reference guide, and written in non-mathematical terms, Structural Equation Modeling: Applications Using Mplus, 2nd edition provides step-by-step instructions of model specification, estimation, evaluation, and modification. Chapters cover: Confirmatory Factor Analysis (CFA); Structural Equation Models (SEM); SEM for Longitudinal Data; Multi-Group Models; Mixture Models; and Power Analysis and Sample Size Estimate for SEM. Presents a useful reference guide for applications of SEM while systematically demonstrating various advanced SEM models Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes Provides step-by-step instructions of model specification and estimation, as well as detailed interpretation of M plus results using real data sets Introduces different methods for sample size estimate and statistical power analysis for SEM Structural Equation Modeling is an excellent book for researchers and graduate students of SEM who want to understand the theory and learn how to build their own SEM models using M plus.

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Группа авторов Maximum Likelihood Estimation and Inference


This book takes a fresh look at the popular and well-established method of maximum likelihood for statistical estimation and inference. It begins with an intuitive introduction to the concepts and background of likelihood, and moves through to the latest developments in maximum likelihood methodology, including general latent variable models and new material for the practical implementation of integrated likelihood using the free ADMB software. Fundamental issues of statistical inference are also examined, with a presentation of some of the philosophical debates underlying the choice of statistical paradigm. Key features: Provides an accessible introduction to pragmatic maximum likelihood modelling. Covers more advanced topics, including general forms of latent variable models (including non-linear and non-normal mixed-effects and state-space models) and the use of maximum likelihood variants, such as estimating equations, conditional likelihood, restricted likelihood and integrated likelihood. Adopts a practical approach, with a focus on providing the relevant tools required by researchers and practitioners who collect and analyze real data. Presents numerous examples and case studies across a wide range of applications including medicine, biology and ecology. Features applications from a range of disciplines, with implementation in R, SAS and/or ADMB. Provides all program code and software extensions on a supporting website. Confines supporting theory to the final chapters to maintain a readable and pragmatic focus of the preceding chapters. This book is not just an accessible and practical text about maximum likelihood, it is a comprehensive guide to modern maximum likelihood estimation and inference. It will be of interest to readers of all levels, from novice to expert. It will be of great benefit to researchers, and to students of statistics from senior undergraduate to graduate level. For use as a course text, exercises are provided at the end of each chapter.

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Mohamed Najim Modeling, Estimation and Optimal Filtration in Signal Processing


The purpose of this book is to provide graduate students and practitioners with traditional methods and more recent results for model-based approaches in signal processing. Firstly, discrete-time linear models such as AR, MA and ARMA models, their properties and their limitations are introduced. In addition, sinusoidal models are addressed. Secondly, estimation approaches based on least squares methods and instrumental variable techniques are presented. Finally, the book deals with optimal filters, i.e. Wiener and Kalman filtering, and adaptive filters such as the RLS, the LMS and their variants.

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Группа авторов Measurement Error Models


The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. «The effort of Professor Fuller is commendable . . . [the book] provides a complete treatment of an important and frequently ignored topic. Those who work with measurement error models will find it valuable. It is the fundamental book on the subject, and statisticians will benefit from adding this book to their collection or to university or departmental libraries.» -Biometrics «Given the large and diverse literature on measurement error/errors-in-variables problems, Fuller's book is most welcome. Anyone with an interest in the subject should certainly have this book.» -Journal of the American Statistical Association «The author is to be commended for providing a complete presentation of a very important topic. Statisticians working with measurement error problems will benefit from adding this book to their collection.» -Technometrics « . . . this book is a remarkable achievement and the product of impressive top-grade scholarly work.» -Journal of Applied Econometrics Measurement Error Models offers coverage of estimation for situations where the model variables are observed subject to measurement error. Regression models are included with errors in the variables, latent variable models, and factor models. Results from several areas of application are discussed, including recent results for nonlinear models and for models with unequal variances. The estimation of true values for the fixed model, prediction of true values under the random model, model checks, and the analysis of residuals are addressed, and in addition, procedures are illustrated with data drawn from nearly twenty real data sets.

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Eugene D. Hahn Bayesian Methods for Management and Business


HIGHLIGHTS THE USE OF BAYESIAN STATISTICS TO GAIN INSIGHTS FROM EMPIRICAL DATA Featuring an accessible approach, Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems demonstrates how Bayesian statistics can help to provide insights into important issues facing business and management. The book draws on multidisciplinary applications and examples and utilizes the freely available software WinBUGS and R to illustrate the integration of Bayesian statistics within data-rich environments. Computational issues are discussed and integrated with coverage of linear models, sensitivity analysis, Markov Chain Monte Carlo (MCMC), and model comparison. In addition, more advanced models including hierarchal models, generalized linear models, and latent variable models are presented to further bridge the theory and application in real-world usage. Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems also features: Numerous real-world examples drawn from multiple management disciplines such as strategy, international business, accounting, and information systems An incremental skill-building presentation based on analyzing data sets with widely applicable models of increasing complexity An accessible treatment of Bayesian statistics that is integrated with a broad range of business and management issues and problems A practical problem-solving approach to illustrate how Bayesian statistics can help to provide insight into important issues facing business and management Bayesian Methods for Management and Business: Pragmatic Solutions for Real Problems is an important textbook for Bayesian statistics courses at the advanced MBA-level and also for business and management PhD candidates as a first course in methodology. In addition, the book is a useful resource for management scholars and practitioners as well as business academics and practitioners who seek to broaden their methodological skill sets.

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(PDF) Latent variable models for ordinal data | Irini ...

Latent variable models for ordinal data. Contributions to Statistics, 2009. Irini Moustaki

Professor Irini Moustaki - LSE Home

She completed her PhD in 1996 in the Department of Statistics at the LSE. She has co-authored books on latent variable models and multivariate data analysis and served as an associate editor and editorial member for a number of scientific journals. She received an honorary doctorate from the Faculty of Social Sciences at the University of Uppsala in 2014 and she is the current editor-in-chief of the journal Psychometrika (2014-2018).

A Latent Variable Model for Ordinal Variables - Irini ...

Irini Moustaki. Applied Psychological Measurement 2016 24: 3, 211-223 Download Citation . If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click on download. Format: Tips on citation download: Download Citation: Download article citation data for: A Latent ...

LSE Statistics - Irini Moustaki

· Moustaki, I. and O’Muircheartaigh, C. (2002) Locating ‘don't know’, ‘no answer’ and middle alternatives on an attitude scale: a latent variable approach, in Latent Variable and Latent Structure Models edited by G. Marcoulides and I. Moustaki, I. Lawrence Erlbaum, 15-41.

Bartholomew, Martin Knott, Irini Moustaki: Latent Variable ...

Bartholomew, Martin Knott, Irini Moustaki: Latent Variable Models and Factor 3e - A Unified Approach. 'Wiley Series in Probability and Statistics'. 3. Auflage. HC gerader Rücken kaschiert. Sprache: Englisch. (Buch (gebunden)) - bei eBook.de

Non-Equivalence of Measurement in Latent Variable ...

Irini Moustaki. London School of Economics & Political Science (LSE) Date Written: September 27, 2013. Abstract. In cross-national surveys and other studies of multiple groups of respondents, an important methodological consideration is the comparability or equivalence of measurement across the groups. Ideally full equivalence would hold, but very often it does not. If non-equivalence of ...

A latent trait and a latent class model for mixed observed ...

Irini Moustaki, Ioulia Papageorgiou, Latent class models for mixed variables with applications in Archaeometry, Computational Statistics & Data Analysis, 10.1016/j.csda.2004.03.001, 48, 3, (659-675), (2005). Crossref. Philippe Huber, O. Scaillet, Maria-Pia Victoria-Feser, A Latent Factor Model for Ordinal Data to Measure Multivariate Predictive Ability of Financial Market Movements, SSRN ...

Bartholomew / Knott / Moustaki | Latent Variable Models ...

Bartholomew / Knott , Latent Variable Models and Factor Analysis, 2011, Buch, 978-0-470-97192-5. Bücher schnell und portofrei

Irini Moustaki (Author of Latent Variable Models and ...

Irini Moustaki is the author of Analysis of Multivariate Social Science Data (2.60 avg rating, 5 ratings, 0 reviews, published 2008), The Analysis and In...

Latent Variable Modelling with Non-Ignorable Item ...

Latent Variable Modelling with Non-Ignorable Item Nonresponse: A General Framework and Multigroup Models for Cross-National Analysis. 32 Pages Posted: 13 Aug 2016. See all articles by Jouni Kuha Jouni Kuha. London School of Economics and Political Science. Myrsini Katsikatsou. London School of Economics & Political Science (LSE) - Department of Statistics. Irini Moustaki. London School of ...

Regional Science Inquiry: Irini Moustaki

Irini Moustaki is a Professor in Social Statistics in the Department of Statistics at the London School of Economics where she was awarded her PhD in 1996. Her main research interests are latent variable models, structural equation models, analysis of categorical data, methods of estimation, goodness-of-fit, detection of outliers and missing values.

Latent Variable and Latent Structure Models | Taylor ...

Latent Variable and Latent Structure Models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come. This volume will be of interest to researchers and practitioners from a wide variety of disciplines, including biology, business, economics, education, medicine, psychology, sociology, and other social and ...

Latent Variable Models and Factor Analysis: A Unified ...

Latent Variable Models and Factor Analysis: A Unified Approach (Wiley Series in Probability and Statistics) | Bartholomew, David J., Knott, Martin, Moustaki, Irini | ISBN: 9780470971925 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Latent Variable and Latent Structure Models Quantitative ...

Latent Variable and Latent Structure Models (Quantitative Methodology Series) | Marcoulides, George A., Moustaki, Irini | ISBN: 9780805840469 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

Irini Moustaki | ACTNext - ACTNextACTNext

Irini Moustaki is a professor of Social Statistics at the London School of Economics and Political Science. She received her Bachelor degree in Statistics and Computer Science from the Athens University of Economics and Business and her MSc and PhD in Statistics from the LSE. Her research interests are in the areas of latent variable models and structural equation models. Her methodological ...

Chapter 2 Latent variable models for ordinal data

Latent variable models for ordinal data Silvia Cagnone, Stefania Mignani and Irini Moustaki 2.1 Introduction Latent variable models with observed ordinal variables are particularly useful for ...

Latent Variable and Latent Structure Models | George A ...

Latent Variable and Latent Structure Models George A. Marcoulides, Irini Moustaki This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling.

A general class of latent variable models for ordinal ...

and latent variables Irini Moustaki Department of Statistics, Athens University of Economics and Business, 76 Patision Street, Athens 104 34 Greece email:moustaki@aueb.gr 1. Abstract Moustaki (2000a) discusses a general class of multi-dimensional la-tent variable models for analyzing ordinal manifest variables. This work is extended here to allow for direct covariate effects on the man-ifest ...

A latent Markov modelling approach to the evaluation of ...

A latent Markov modelling approach to the evaluation of circulating cathodic antigen strips for schistosomiasis diagnosis pre- and post-praziquantel treatment in Uganda . Article (Accepted version) (Refereed) Original citation: Tanaka, Mark M., Koukounari, Artemis, Donnelly, Christl A., Moustaki, Irini, Tukahebwa, Edridah

Latent Variable Models and Factor Analysis (eBook, PDF ...

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques for investigating dependency. This book: * Provides a unified approach showing how such apparently ...

(PDF) Generalized latent trait models | Irini Moustaki ...

Irini Moustaki. M. Knott. Download PDF. Download Full PDF Package. This paper. A short summary of this paper. 37 Full PDFs related to this paper. READ PAPER. Generalized latent trait models. Download. Generalized latent trait models ...

Latent Variable Models and Factor Analysis: A Unified ...

David J. Bartholomew, Martin Knott, Irini Moustaki. Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent ...

David J. Bartholomew, Martin Knott, Irini Moustaki: Latent ...

David J. Bartholomew, Martin Knott, Irini Moustaki: Latent Variable Models and Factor Analysis - A Unified Approach. Sprache: Englisch. (eBook epub) - bei eBook.de

Latent variable models for mixed categorical and survival ...

In this article, we discuss a latent variable model with continuous latent variables for manifest variables that are a mixture of categorical and survival outcomes. Models for censored and uncensor...

Silia Vitoratou, Ioannis Ntzoufras and Irini Moustaki ...

Silia Vitoratou, Ioannis Ntzoufras and Irini Moustaki. Abstract In latent variable models parameter estimation can be implemented by using the joint or the marginal likelihood, based on independence or conditional independence assumptions. The same dilemma occurs within the Bayesian framework with respect to the estimation of the Bayesian marginal (or integrated) likelihood, which is the main ...

Latent Variable Models and Factor Analysis

Martin Knott, Irini Moustaki. p. cm. Includes bibliographical references and index. ISBN 978-0-470-97192-5 (cloth) 1. Latent variables. 2. Latent structure analysis. 3. Factor analysis. I. Knott, M. (Martin) II. Moustaki, Irini. III. Title. QA278.6.B37 2011 519.5 35–dc22 2011007711 A catalogue record for this book is available from the British Library. Print ISBN: 978-0-470-97192-5 ePDF ISBN ...

Wiley Series in Probability and Statistics: Latent ...

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples.

Weighting for item non‐response in attitude scales by ...

Jouni Kuha, Myrsini Katsikatsou, Irini Moustaki, Latent variable modelling with non‐ignorable item non‐response: multigroup response propensity models for cross‐national analysis, Journal of the Royal Statistical Society: Series A (Statistics in Society), 10.1111/rssa.12350, 181, 4, (1169-1192), (2018).

Latent variable models for multivariate longitudinal ...

The paper proposes a full information maximum likelihood estimation method for modelling multivariate longitudinal ordinal variables. Two latent variable models are proposed that account for depend...

Latent Variable Models and Factor Analysis Wiley Series in ...

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques ...

Latent Variable and Latent Structure Models | Marcoulides ...

Latent Variable and Latent Structure Models | Marcoulides, George A.; Moustaki, Irini | download | B–OK. Download books for free. Find books

Jouni Kuha, Irini Moustaki Non-equivalence of measurement ...

Non-equivalence of measurement in latent variable modeling of multigroup data: A sensitivity analysis Jouni Kuha1 Irini Moustaki London School of Economics 1Correspondence Author: Department of Statistics, London School of Economics, Houghton Street, London WC2A 2AE, United Kingdom, email: j.kuha@lse.ac.uk, tel: +44 (0)20 7955 6835. This research was funded by the Economic and Social Research ...

Latent Variable Models and Factor Analysis: A Unified ...

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques ...

Latent Variable And Latent Structure Models Marcoulides ...

Latent Variable And Latent Structure Models Marcoulides George A Moustaki Irini.pdf amazon: latent variable and latent structure models latent variable and latent structure models' thought-provoking chapters from the leading researchers in the area will help to stimulate ideas for further research for many years to come. this volume will be of interest to researchers and practitioners from a ...

Latent Variable Models And Factor Analysis Kendalls ...

latent variable models and factor analysis kendalls library of statistics 7 Nov 23, 2020 Posted By Karl May Public Library TEXT ID 6759b33d Online PDF Ebook Epub Library reason for it to move closer to centre stage in the amazoncom latent variable models and factor analysis kendalls library of statistics 7 9780470711101 bartholomew david j knott martin books youre already purchasing the item ...

A goodness of fit test for sparse 2p contingency tables ...

Olufunmilola Ibironke, Artemis Koukounari, Samuel Asaolu, Irini Moustaki, Clive Shiff, Validation of a New Test for Schistosoma haematobium Based on Detection of Dra1 DNA Fragments in Urine: Evaluation through Latent Class Analysis, PLoS Neglected Tropical Diseases, 10.1371/journal.pntd.0001464, 6, 1, (e1464), (2012).

‪Irini Moustaki‬ - ‪Google Scholar‬

Irini Moustaki. Professor in Social Statistics, London School of Economics and Political Science. Verified email at lse.ac.uk - Homepage. statistics psychometrics. Articles Cited by Co-authors. Title. Sort. Sort by citations Sort by year Sort by title. Cited by. Cited by. Year; Latent variable models and factor analysis: A unified approach. DJ Bartholomew, M Knott, I Moustaki. John Wiley ...

Wiley Series in Probability and Statistics - Latent ...

Latent Variable Models and Factor Analysis provides a comprehensive and unified approach to factor analysis and latent variable modeling from a statistical perspective. This book presents a general framework to enable the derivation of the commonly used models, along with updated numerical examples. Nature and interpretation of a latent variable is also introduced along with related techniques ...

Latent Variable and Latent Structure Models by Marcoulides ...

Buy Latent Variable and Latent Structure Models by Marcoulides, George A., Moustaki, Irini online on Amazon.ae at best prices. Fast and free shipping free returns cash on delivery available on eligible purchase.

Moustaki

A latent variable approach is adopted for handling non-ignorable item non-response and drop out. Various model specifications are proposed to model the missing data mechanism together with the measurement and structural model. The model for the missing data mechanism will serve two purposes: first to characterize the item nonresponse/ drop-out as ignorable or non-ignorable and consequently to ...

Latent Variable and Latent Structure Models: Marcoulides ...

Latent Variable and Latent Structure Models: Marcoulides, George A., Moustaki, Irini: Amazon.sg: Books

September 1997 VITA: Irini Moustaki

Moustaki, I. and Papageorgiou, I. (2004) Latent class models for mixed outcomes with applications in Archaeometry. Computational Statistics and Data Analysis, Vol. 48(3), 659-675. Moustaki, I. and Joreskog, K. and Mavridis, D. (2004) Factor models for ordinal variables with covariate effects on the manifest and latent variables: A comparison of LISREL and IRT approaches. Structural Equation ...

Latent Variable Models and Factor Analysis: A Unified ...

Buy Latent Variable Models and Factor Analysis: A Unified Approach by Bartholomew, David J., Knott, Martin, Moustaki, Irini online on Amazon.ae at best prices. Fast and free shipping free returns cash on delivery available on eligible purchase.

Latent Variable Models and Factor Analysis: A Unified ...

Latent Variable Models and Factor Analysis: A Unified Approach (Wiley Series in Probability and Statistics Book 900) - Kindle edition by Bartholomew, David J., Knott, Martin, Moustaki, Irini. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Latent Variable Models and Factor Analysis: A Unified ...

NAME Irini Moustaki PRESENT Associate Professor in Statistics

VITA: Irini Moustaki 2 PUBLISHED ARTICLES 1. Moustaki, I. (1996) A latent trait and a latent class model for mixed observed variables. British Journal of Mathematical and Statistical Psychology, 49, 313-334. 2. O’Muircheartaigh, O. and Moustaki, I. (1999). Symmetric pattern models: a latent variable approach to item non-response in attitude scales. Journal of the Royal Statistical Society ...

Description: Latent variable models and factor analysis

Latent variable models and factor analysis : a unified approach [E-Book] / David Bartholomew, Martin Knott, Irini Moustaki. Latent variable models and factor analysis : a unified approach [E-Book] / David Bartholomew, Martin Knott, Irini Moustaki.

12 References | Handbook of Regression Modeling in People ...

8 Modeling Explicit and Latent Hierarchical Structure in Data; 9 Survival Analysis for Modeling the Occurrence of Singular Events Over Time; 10 Alternative Technical Approaches in R and Python; 11 Power Analysis to Estimate Required Sample Sizes for Inferential Modeling; 12 References; View book source . 12 References. Agresti, Alan. 2007. An Introduction to Categorical Data Analysis ...

Kattamuri S. Sarma Predictive Modeling with SAS Enterprise Miner


A step-by-step guide to predictive modeling!


Kattamuri Sarma's Predictive Modeling with SAS Enterprise Miner: Practical Solutions for Business Applications, Third Edition, will show you how to develop and test predictive models quickly using SAS Enterprise Miner. Using realistic data, the book explains complex methods in a simple and practical way to readers from different backgrounds and industries. Incorporating the latest version of Enterprise Miner, this third edition also expands the section on time series.


Written for business analysts, data scientists, statisticians, students, predictive modelers, and data miners, this comprehensive text provides examples that will strengthen your understanding of the essential concepts and methods of predictive modeling. Topics covered include logistic regression, regression, decision trees, neural networks, variable clustering, observation clustering, data imputation, binning, data exploration, variable selection, variable transformation, and much more, including analysis of textual data.


Develop predictive models quickly, learn how to test numerous models and compare the results, gain an in-depth understanding of predictive models and multivariate methods, and discover how to do in-depth analysis. Do it all with Predictive Modeling with SAS Enterprise Miner!

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Gesong Chen Variable Frequency Transformers for Large Scale Power Systems Interconnection. Theory and Applications


This book is an all-in-one resource on the development and application of variable frequency transformers to power systems and smart grids. It introduces the main technical issues of variable frequency transformers (VFT) systematically, including its basic construction, theory equations, and simulation models. Readers will then gain an in-depth discussion of its control system, operation performance, low frequency power oscillation, and technical economics, before proceeding to practical implementation and future developments. The related concepts of energy revolution, third generation grids, and power system interconnection are discussed as well. The first, comprehensive introduction to variable frequency transformers (VFT) An in-depth look at the construction of VFT, with simulations and applications Demonstrates how to assess the control system and overall system performance Analyses future developments, energy revolution and power system interconnections Variable Frequency Transformers for Large Scale Power Systems is a timely overview of the state of the art for VFT as it is increasingly adopted in smart grids. It is intended for engineers and researchers specializing in power system planning and operation, as well as advanced students and industry professionals of power engineering.

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M.A. Nikki Moustaki 1984

Harry G. Perros VBR Video Traffic Models


There has been a phenomenal growth in video applications over the past few years. An accurate traffic model of Variable Bit Rate (VBR) video is necessary for performance evaluation of a network design and for generating synthetic traffic that can be used for benchmarking a network. A large number of models for VBR video traffic have been proposed in the literature for different types of video in the past 20 years. Here, the authors have classified and surveyed these models and have also evaluated the models for H.264 AVC and MVC encoded video and discussed their findings.

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Группа авторов Computational Models of Brain and Behavior


A comprehensive Introduction to the world of brain and behavior computational models This book provides a broad collection of articles covering different aspects of computational modeling efforts in psychology and neuroscience. Specifically, it discusses models that span different brain regions (hippocampus, amygdala, basal ganglia, visual cortex), different species (humans, rats, fruit flies), and different modeling methods (neural network, Bayesian, reinforcement learning, data fitting, and Hodgkin-Huxley models, among others). Computational Models of Brain and Behavior is divided into four sections: (a) Models of brain disorders; (b) Neural models of behavioral processes; (c) Models of neural processes, brain regions and neurotransmitters, and (d) Neural modeling approaches. It provides in-depth coverage of models of psychiatric disorders, including depression, posttraumatic stress disorder (PTSD), schizophrenia, and dyslexia; models of neurological disorders, including Alzheimer’s disease, Parkinson’s disease, and epilepsy; early sensory and perceptual processes; models of olfaction; higher/systems level models and low-level models; Pavlovian and instrumental conditioning; linking information theory to neurobiology; and more. Covers computational approximations to intellectual disability in down syndrome Discusses computational models of pharmacological and immunological treatment in Alzheimer's disease Examines neural circuit models of serotonergic system (from microcircuits to cognition) Educates on information theory, memory, prediction, and timing in associative learning Computational Models of Brain and Behavior is written for advanced undergraduate, Master's and PhD-level students—as well as researchers involved in computational neuroscience modeling research.

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Группа авторов Advanced Time Series Data Analysis. Forecasting Using EViews


Introduces the latest developments in forecasting in advanced quantitative data analysis This book presents advanced univariate multiple regressions, which can directly be used to forecast their dependent variables, evaluate their in-sample forecast values, and compute forecast values beyond the sample period. Various alternative multiple regressions models are presented based on a single time series, bivariate, and triple time-series, which are developed by taking into account specific growth patterns of each dependent variables, starting with the simplest model up to the most advanced model. Graphs of the observed scores and the forecast evaluation of each of the models are offered to show the worst and the best forecast models among each set of the models of a specific independent variable. Advanced Time Series Data Analysis: Forecasting Using EViews provides readers with a number of modern, advanced forecast models not featured in any other book. They include various interaction models, models with alternative trends (including the models with heterogeneous trends), and complete heterogeneous models for monthly time series, quarterly time series, and annually time series. Each of the models can be applied by all quantitative researchers. Presents models that are all classroom tested Contains real-life data samples Contains over 350 equation specifications of various time series models Contains over 200 illustrative examples with special notes and comments Applicable for time series data of all quantitative studies Advanced Time Series Data Analysis: Forecasting Using EViews will appeal to researchers and practitioners in forecasting models, as well as those studying quantitative data analysis. It is suitable for those wishing to obtain a better knowledge and understanding on forecasting, specifically the uncertainty of forecast values.

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Группа авторов Structural Equation Modeling


***Winner of the 2008 Ziegel Prize for outstanding new book of the year*** Structural equation modeling (SEM) is a powerful multivariate method allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking measurement errors into account. As SEMs have grown in popularity in recent years, new models and statistical methods have been developed for more accurate analysis of more complex data. A Bayesian approach to SEMs allows the use of prior information resulting in improved parameter estimates, latent variable estimates, and statistics for model comparison, as well as offering more reliable results for smaller samples. Structural Equation Modeling introduces the Bayesian approach to SEMs, including the selection of prior distributions and data augmentation, and offers an overview of the subject’s recent advances. Demonstrates how to utilize powerful statistical computing tools, including the Gibbs sampler, the Metropolis-Hasting algorithm, bridge sampling and path sampling to obtain the Bayesian results. Discusses the Bayes factor and Deviance Information Criterion (DIC) for model comparison. Includes coverage of complex models, including SEMs with ordered categorical variables, and dichotomous variables, nonlinear SEMs, two-level SEMs, multisample SEMs, mixtures of SEMs, SEMs with missing data, SEMs with variables from an exponential family of distributions, and some of their combinations. Illustrates the methodology through simulation studies and examples with real data from business management, education, psychology, public health and sociology. Demonstrates the application of the freely available software WinBUGS via a supplementary website featuring computer code and data sets. Structural Equation Modeling: A Bayesian Approach is a multi-disciplinary text ideal for researchers and students in many areas, including: statistics, biostatistics, business, education, medicine, psychology, public health and social science.

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Harvey Goldstein Multilevel Statistical Models


Throughout the social, medical and other sciences the importance of understanding complex hierarchical data structures is well understood. Multilevel modelling is now the accepted statistical technique for handling such data and is widely available in computer software packages. A thorough understanding of these techniques is therefore important for all those working in these areas. This new edition of Multilevel Statistical Models brings these techniques together, starting from basic ideas and illustrating how more complex models are derived. Bayesian methodology using MCMC has been extended along with new material on smoothing models, multivariate responses, missing data, latent normal transformations for discrete responses, structural equation modeling and survival models. Key Features: Provides a clear introduction and a comprehensive account of multilevel models. New methodological developments and applications are explored. Written by a leading expert in the field of multilevel methodology. Illustrated throughout with real-life examples, explaining theoretical concepts. This book is suitable as a comprehensive text for postgraduate courses, as well as a general reference guide. Applied statisticians in the social sciences, economics, biological and medical disciplines will find this book beneficial.

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Nikolaos Limnios Introduction to Stochastic Models


This book provides a pedagogical examination of the way in which stochastic models are encountered in applied sciences and techniques such as physics, engineering, biology and genetics, economics and social sciences. It covers Markov and semi-Markov models, as well as their particular cases: Poisson, renewal processes, branching processes, Ehrenfest models, genetic models, optimal stopping, reliability, reservoir theory, storage models, and queuing systems. Given this comprehensive treatment of the subject, students and researchers in applied sciences, as well as anyone looking for an introduction to stochastic models, will find this title of invaluable use.

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Группа авторов Nonlinear Statistical Models


A comprehensive text and reference bringing together advances in the theory of probability and statistics and relating them to applications. The three major categories of statistical models that relate dependent variables to explanatory variables are covered: univariate regression models, multivariate regression models, and simultaneous equations models. Methods are illustrated with worked examples, complete with figures that display code and output.

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