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Three Contributions To Latent Variable Modeling
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Book Synopsis Three Contributions to Latent Variable Modeling by : Xiang Liu
Download or read book Three Contributions to Latent Variable Modeling written by Xiang Liu and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The dissertation includes three papers that address some theoretical and technical issues of latent variable models. The first paper extends the uniformly most powerful test approach for testing person parameter in IRT to the two-parameter logistic models. In addition, an efficient branch-and-bound algorithm for computing the exact p-value is proposed. The second paper proposes a reparameterization of the log-linear CDM model. A Gibbs sampler is developed for posterior computation. The third paper proposes an ordered latent class model with infinite classes using a stochastic process prior. Furthermore, a nonparametric IRT application is also discussed.
Book Synopsis Contributions to Latent Variable Modeling in Educational Measurement by : Robert Johannes Zwitser
Download or read book Contributions to Latent Variable Modeling in Educational Measurement written by Robert Johannes Zwitser and published by . This book was released on 2015 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Latent Variable and Latent Structure Models by : George A. Marcoulides
Download or read book Latent Variable and Latent Structure Models written by George A. Marcoulides and published by Psychology Press. This book was released on 2014-04-04 with total page 331 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited volume features cutting-edge topics from the leading researchers in the areas of latent variable modeling. Content highlights include coverage of approaches dealing with missing values, semi-parametric estimation, robust analysis, hierarchical data, factor scores, multi-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two traditions: psychometrics and structural equation modeling. 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 behavioral sciences. A working knowledge of basic multivariate statistics and measurement theory is assumed.
Book Synopsis Latent Variable Modeling with R by : W. Holmes Finch
Download or read book Latent Variable Modeling with R written by W. Holmes Finch and published by Routledge. This book was released on 2015-06-26 with total page 341 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book demonstrates how to conduct latent variable modeling (LVM) in R by highlighting the features of each model, their specialized uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis of the data using R, the relevant theory, the assumptions underlying the model, and other statistical details to help readers better understand the models and interpret the results. Every R command necessary for conducting the analyses is described along with the resulting output which provides readers with a template to follow when they apply the methods to their own data. The basic information pertinent to each model, the newest developments in these areas, and the relevant R code to use them are reviewed. Each chapter also features an introduction, summary, and suggested readings. A glossary of the text’s boldfaced key terms and key R commands serve as helpful resources. The book is accompanied by a website with exercises, an answer key, and the in-text example data sets. Latent Variable Modeling with R: -Provides some examples that use messy data providing a more realistic situation readers will encounter with their own data. -Reviews a wide range of LVMs including factor analysis, structural equation modeling, item response theory, and mixture models and advanced topics such as fitting nonlinear structural equation models, nonparametric item response theory models, and mixture regression models. -Demonstrates how data simulation can help researchers better understand statistical methods and assist in selecting the necessary sample size prior to collecting data. -www.routledge.com/9780415832458 provides exercises that apply the models along with annotated R output answer keys and the data that corresponds to the in-text examples so readers can replicate the results and check their work. The book opens with basic instructions in how to use R to read data, download functions, and conduct basic analyses. From there, each chapter is dedicated to a different latent variable model including exploratory and confirmatory factor analysis (CFA), structural equation modeling (SEM), multiple groups CFA/SEM, least squares estimation, growth curve models, mixture models, item response theory (both dichotomous and polytomous items), differential item functioning (DIF), and correspondance analysis. The book concludes with a discussion of how data simulation can be used to better understand the workings of a statistical method and assist researchers in deciding on the necessary sample size prior to collecting data. A mixture of independently developed R code along with available libraries for simulating latent models in R are provided so readers can use these simulations to analyze data using the methods introduced in the previous chapters. Intended for use in graduate or advanced undergraduate courses in latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, and social and health sciences, researchers in these fields also appreciate this book’s practical approach. The book provides sufficient conceptual background information to serve as a standalone text. Familiarity with basic statistical concepts is assumed but basic knowledge of R is not.
Book Synopsis Latent Variable Modeling with Slowness, Monotonicity, and Impulsivity Features by : Ranjith Ravi Kumar Chiplunkar
Download or read book Latent Variable Modeling with Slowness, Monotonicity, and Impulsivity Features written by Ranjith Ravi Kumar Chiplunkar and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data-driven modeling has been finding increasing prominence in process systems engineering in both academia and industries. Latent variable modeling forms an important component of data-driven modeling. Through latent variable modeling, not only can we deal with issues such as collinearity, noise, and high data dimensionality, but we can also impart the "notions" that we have regarding the process into the model. Imparting such notions makes the latent variables process-relevant and enhances the accuracy of the models. This thesis explores such ways of making the latent variables process-relevant by incorporating aspects such as slowness, monotonicity, and impulsivity that are commonly observed in various industrial processes. Many chemical engineering processes are typically characterized primarily by slow variations. The latent variables of such processes will be characterized by high tem poral correlation or low velocities. This aspect is considered in slow feature analysis which is a latent variable method that aims to extract slowly varying features. Since the basic version of slow feature analysis is unsupervised, the first contribution of the thesis explores supervised learning of slow features through a linear model. In this case, the objective function of the slow feature analysis method is modified by adding a term that maximizes the correlation of the slow features with the output variables. Two such formulations are proposed to achieve the required objective and corresponding algorithms to achieve each objective are proposed. The second contribution extends the supervised slow feature extraction problem to a nonlinear case. The nonlinearity is achieved through the usage of Siamese neural networks. Siamese neural networks contain two identical networks that give them the ability to handle two samples at a time. Since the objective of slow feature analysis is to reduce the velocity of the latent variables, it needs to handle two samples simultaneously. Hence, this work uses the Siamese networks to perform supervised slow feature analysis. The third contribution of the thesis considers the monotonicity aspect in latent variable modeling for degrading processes. Processes that have degradation in either equipment or the quality of the process are overall non-stationarity in nature. Since degradation or damage usually evolves monotonically, latent variable modeling of such processes needs to include the monotonicity condition. This work proposes a state-space model to characterize such systems where the latent variable corresponding to the degrading component is modeled using a closed skew-normal random walk model, and other stationary variations are modeled by a Gaussian dynamic model. Here, the objective is to separate the monotonically degrading component of the data from other stationary variations for effective monitoring of the process. The resulting simultaneous state-and-parameter estimation problem is solved using the expectation-maximization approach and the smoothing algorithm is rigorously derived for a system defined by a closed skew-normal distribution random walk model. In the fourth contribution of the thesis, processes that are characterized by sudden or impulsive changes are studied. To characterize such behaviors the system is modeled using a state-space model with the dynamics of one of the states being defined by a Cauchy distribution. Since the Cauchy distribution has a fat tail, it can model the sudden jumps in a process. Hence, the resulting model has a mix of Cauchy and Gaussian latent variables, where the Cauchy latent variable models the sudden jumps and the Gaussian latent variables model other variations. The states and parame ters of the resulting model are identified in a Bayesian manner using the variational Bayesian inference framework. The efficacy of all the contributions is verified through both numerical and relevant industrial case studies.
Book Synopsis Handbook of Latent Variable and Related Models by :
Download or read book Handbook of Latent Variable and Related Models written by and published by Elsevier. This book was released on 2011-08-11 with total page 458 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Handbook covers latent variable models, which are a flexible class of models for modeling multivariate data to explore relationships among observed and latent variables. - Covers a wide class of important models - Models and statistical methods described provide tools for analyzing a wide spectrum of complicated data - Includes illustrative examples with real data sets from business, education, medicine, public health and sociology. - Demonstrates the use of a wide variety of statistical, computational, and mathematical techniques.
Book Synopsis Advances in Latent Class Analysis by : Gregory R. Hancock
Download or read book Advances in Latent Class Analysis written by Gregory R. Hancock and published by IAP. This book was released on 2019-05-01 with total page 281 pages. Available in PDF, EPUB and Kindle. Book excerpt: What is latent class analysis? If you asked that question thirty or forty years ago you would have gotten a different answer than you would today. Closer to its time of inception, latent class analysis was viewed primarily as a categorical data analysis technique, often framed as a factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests within much broader mixture and diagnostic modeling framework, integrating measured and latent variables that may be categorical and/or continuous, and where latent classes serve to define the subpopulations for whom many aspects of the focal measured and latent variable model may differ. For latent class analysis to take these developmental leaps required contributions that were methodological, certainly, as well as didactic. Among the leaders on both fronts was C. Mitchell “Chan” Dayton, at the University of Maryland, whose work in latent class analysis spanning several decades helped the method to expand and reach its current potential. The current volume in the Center for Integrated Latent Variable Research (CILVR) series reflects the diversity that is latent class analysis today, celebrating work related to, made possible by, and inspired by Chan’s noted contributions, and signaling the even more exciting future yet to come.
Book Synopsis Advances in Latent Variables by : Maurizio Carpita
Download or read book Advances in Latent Variables written by Maurizio Carpita and published by Springer. This book was released on 2015-04-01 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: The book, belonging to the series “Studies in Theoretical and Applied Statistics– Selected Papers from the Statistical Societies”, presents a peer-reviewed selection of contributions on relevant topics organized by the editors on the occasion of the SIS 2013 Statistical Conference "Advances in Latent Variables. Methods, Models and Applications", held at the Department of Economics and Management of the University of Brescia from June 19 to 21, 2013. The focus of the book is on advances in statistical methods for analyses with latent variables. In fact, in recent years, there has been increasing interest in this broad research area from both a theoretical and an applied point of view, as the statistical latent variable approach allows the effective modeling of complex real-life phenomena in a wide range of research fields. A major goal of the volume is to bring together articles written by statisticians from different research fields, which present different approaches and experiences related to the analysis of unobservable variables and the study of the relationships between them.
Book Synopsis Latent Variable Path Modeling with Partial Least Squares by : Jan-Bernd Lohmöller
Download or read book Latent Variable Path Modeling with Partial Least Squares written by Jan-Bernd Lohmöller and published by Springer Science & Business Media. This book was released on 2013-11-11 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: Partial Least Squares (PLS) is an estimation method and an algorithm for latent variable path (LVP) models. PLS is a component technique and estimates the latent variables as weighted aggregates. The implications of this choice are considered and compared to covariance structure techniques like LISREL, COSAN and EQS. The properties of special cases of PLS (regression, factor scores, structural equations, principal components, canonical correlation, hierarchical components, correspondence analysis, three-mode path and component analysis) are examined step by step and contribute to the understanding of the general PLS technique. The proof of the convergence of the PLS algorithm is extended beyond two-block models. Some 10 computer programs and 100 applications of PLS are referenced. The book gives the statistical underpinning for the computer programs PLS 1.8, which is in use in some 100 university computer centers, and for PLS/PC. It is intended to be the background reference for the users of PLS 1.8, not as textbook or program manual.
Book Synopsis Contributions to Latent Variables in Structural Equation Models and to Causal Mediation Models by : Axel Mayer
Download or read book Contributions to Latent Variables in Structural Equation Models and to Causal Mediation Models written by Axel Mayer and published by . This book was released on 2013 with total page 290 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Latent Variable Modeling Using R by : A. Alexander Beaujean
Download or read book Latent Variable Modeling Using R written by A. Alexander Beaujean and published by Routledge. This book was released on 2014-05-09 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: This step-by-step guide is written for R and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the lavaan package, this book is designed to help readers quickly understand LVMs and their analysis in R. The author reviews the reasoning behind the syntax selected and provides examples that demonstrate how to analyze data for a variety of LVMs. Featuring examples applicable to psychology, education, business, and other social and health sciences, minimal text is devoted to theoretical underpinnings. The material is presented without the use of matrix algebra. As a whole the book prepares readers to write about and interpret LVM results they obtain in R. Each chapter features background information, boldfaced key terms defined in the glossary, detailed interpretations of R output, descriptions of how to write the analysis of results for publication, a summary, R based practice exercises (with solutions included in the back of the book), and references and related readings. Margin notes help readers better understand LVMs and write their own R syntax. Examples using data from published work across a variety of disciplines demonstrate how to use R syntax for analyzing and interpreting results. R functions, syntax, and the corresponding results appear in gray boxes to help readers quickly locate this material. A unique index helps readers quickly locate R functions, packages, and datasets. The book and accompanying website at http://blogs.baylor.edu/rlatentvariable/ provides all of the data for the book’s examples and exercises as well as R syntax so readers can replicate the analyses. The book reviews how to enter the data into R, specify the LVMs, and obtain and interpret the estimated parameter values. The book opens with the fundamentals of using R including how to download the program, use functions, and enter and manipulate data. Chapters 2 and 3 introduce and then extend path models to include latent variables. Chapter 4 shows readers how to analyze a latent variable model with data from more than one group, while Chapter 5 shows how to analyze a latent variable model with data from more than one time period. Chapter 6 demonstrates the analysis of dichotomous variables, while Chapter 7 demonstrates how to analyze LVMs with missing data. Chapter 8 focuses on sample size determination using Monte Carlo methods, which can be used with a wide range of statistical models and account for missing data. The final chapter examines hierarchical LVMs, demonstrating both higher-order and bi-factor approaches. The book concludes with three Appendices: a review of common measures of model fit including their formulae and interpretation; syntax for other R latent variable models packages; and solutions for each chapter’s exercises. Intended as a supplementary text for graduate and/or advanced undergraduate courses on latent variable modeling, factor analysis, structural equation modeling, item response theory, measurement, or multivariate statistics taught in psychology, education, human development, business, economics, and social and health sciences, this book also appeals to researchers in these fields. Prerequisites include familiarity with basic statistical concepts, but knowledge of R is not assumed.
Book Synopsis Latent Variable Models and Factor Analysis by : David J. Bartholomew
Download or read book Latent Variable Models and Factor Analysis written by David J. Bartholomew and published by John Wiley & Sons. This book was released on 2011-06-28 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis Structural Equations with Latent Variables by : Kenneth A. Bollen
Download or read book Structural Equations with Latent Variables written by Kenneth A. Bollen and published by John Wiley & Sons. This book was released on 2014-08-28 with total page 528 pages. Available in PDF, EPUB and Kindle. Book excerpt: Analysis of Ordinal Categorical Data Alan Agresti Statistical Science Now has its first coordinated manual of methods for analyzing ordered categorical data. This book discusses specialized models that, unlike standard methods underlying nominal categorical data, efficiently use the information on ordering. It begins with an introduction to basic descriptive and inferential methods for categorical data, and then gives thorough coverage of the most current developments, such as loglinear and logit models for ordinal data. Special emphasis is placed on interpretation and application of methods and contains an integrated comparison of the available strategies for analyzing ordinal data. This is a case study work with illuminating examples taken from across the wide spectrum of ordinal categorical applications. 1984 (0 471-89055-3) 287 pp. Regression Diagnostics Identifying Influential Data and Sources of Collinearity David A. Belsley, Edwin Kuh and Roy E. Welsch This book provides the practicing statistician and econometrician with new tools for assessing the quality and reliability of regression estimates. Diagnostic techniques are developed that aid in the systematic location of data points that are either unusual or inordinately influential; measure the presence and intensity of collinear relations among the regression data and help to identify the variables involved in each; and pinpoint the estimated coefficients that are potentially most adversely affected. The primary emphasis of these contributions is on diagnostics, but suggestions for remedial action are given and illustrated. 1980 (0 471-05856-4) 292 pp. Applied Regression Analysis Second Edition Norman Draper and Harry Smith Featuring a significant expansion of material reflecting recent advances, here is a complete and up-to-date introduction to the fundamentals of regression analysis, focusing on understanding the latest concepts and applications of these methods. The authors thoroughly explore the fitting and checking of both linear and nonlinear regression models, using small or large data sets and pocket or high-speed computing equipment. Features added to this Second Edition include the practical implications of linear regression; the Durbin-Watson test for serial correlation; families of transformations; inverse, ridge, latent root and robust regression; and nonlinear growth models. Includes many new exercises and worked examples. 1981 (0 471-02995-5) 709 pp.
Book Synopsis Latent Variable Models and Factor Analysis by : David J. Bartholomew
Download or read book Latent Variable Models and Factor Analysis written by David J. Bartholomew and published by Wiley. This book was released on 1999-08-10 with total page 214 pages. Available in PDF, EPUB and Kindle. Book excerpt: Hitherto latent variable modelling has hovered on the fringes of the statistical mainstream but if the purpose of statistics is to deal with real problems, there is every reason for it to move closer to centre stage. In the social sciences especially, latent variables are common and if they are to be handled in a truly scientific manner, statistical theory must be developed to include them. This book aims to show how that should be done. This second edition is a complete re-working of the book of the same name which appeared in the Griffin’s Statistical Monographs in 1987. Since then there has been a surge of interest in latent variable methods which has necessitated a radical revision of the material but the prime object of the book remains the same. It provides a unified and coherent treatment of the field from a statistical perspective. This is achieved by setting up a sufficiently general framework to enable the derivation of the commonly used models. The subsequent analysis is then done wholly within the realm of probability calculus and the theory of statistical inference. Numerical examples are provided as well as the software to carry them out ( where this is not otherwise available). Additional data sets are provided in some cases so that the reader can aquire a wider experience of analysis and interpretation.
Book Synopsis Latent Variable Models by : John C. Loehlin
Download or read book Latent Variable Models written by John C. Loehlin and published by Psychology Press. This book was released on 2004-05-20 with total page 303 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book introduces multiple-latent variable models by utilizing path diagrams to explain the underlying relationships in the models. This approach helps less mathematically inclined students grasp the underlying relationships between path analysis, factor analysis, and structural equation modeling more easily. A few sections of the book make use of elementary matrix algebra. An appendix on the topic is provided for those who need a review. The author maintains an informal style so as to increase the book's accessibility. Notes at the end of each chapter provide some of the more technical details. The book is not tied to a particular computer program, but special attention is paid to LISREL, EQS, AMOS, and Mx. New in the fourth edition of Latent Variable Models: *a data CD that features the correlation and covariance matrices used in the exercises; *new sections on missing data, non-normality, mediation, factorial invariance, and automating the construction of path diagrams; and *reorganization of chapters 3-7 to enhance the flow of the book and its flexibility for teaching. Intended for advanced students and researchers in the areas of social, educational, clinical, industrial, consumer, personality, and developmental psychology, sociology, political science, and marketing, some prior familiarity with correlation and regression is helpful.
Book Synopsis Using Latent Variable Models to Improve Causal Estimation by : Huseyin Oktay
Download or read book Using Latent Variable Models to Improve Causal Estimation written by Huseyin Oktay and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Estimating the causal effect of a treatment from data has been a key goal for a large number of studies in many domains. Traditionally, researchers use carefully designed randomized experiments for causal inference. However, such experiments can not only be costly in terms of time and money but also infeasible for some causal questions. To overcome these challenges, causal estimation methods from observational data have been developed by researchers from diverse disciplines and increasingly studies using such methods account for a large share in empirical work. Such growing interest has also brought together two arguably separate fields: machine learning and causal estimation, and this thesis also contributes to this intersection. Specifically, in observational data researchers have lack of control over the data generation process. This results in a fundamental challenge: the presence of confounder variables (i.e., variables that affect both treatment and outcome). Such variables, when not adjusted statistically, can result in biased causal estimates. When confounder variables are observed, many methods can be used to adjust for their effect. However, in most real world observational data sets, accurately measuring all potential confounder variables is far from feasible, hence important confounder variables are likely to remain unobserved. The central idea of this thesis is to explicitly account for unobserved confounders by inferring their values using a predictive model. This thesis presents three main contributions in the intersection of machine learning and causal estimation. First, we present one of the earliest application of causal estimation methods from social sciences to social media platforms to answer three causal questions. Second, we present a novel generative model for estimating ordinal variables with distant supervision. We also apply this model to data from US Twitter user population and discover variation in behavior among users from different age groups. Third, we characterize the behavior of an effect restoration model based on graphical models with theoretical analysis and simulation studies. We also apply this effect restoration model with predictive models to account for unobserved confounder variables.
Book Synopsis Current Topics in the Theory and Application of Latent Variable Models by : Michael Charles Edwards
Download or read book Current Topics in the Theory and Application of Latent Variable Models written by Michael Charles Edwards and published by Routledge. This book was released on 2013 with total page 298 pages. Available in PDF, EPUB and Kindle. Book excerpt: First Published in 2013. Routledge is an imprint of Taylor & Francis, an informa company.
