Latent Variables in Linear Stochastic Models PDF Download
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Author: Taeke Klaas Dijkstra
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 200
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Book Description
Author: Taeke Klaas Dijkstra
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 202
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Book Description
Author: Maurizio Carpita
Publisher: Springer
ISBN: 3319029673
Category : Mathematics
Languages : en
Pages : 284
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Book Description
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.
Author: Taeke Klaas Dijkstra
Publisher:
ISBN:
Category : Analysis of variance
Languages : en
Pages : 200
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Book Description
Author: Klaus Haagen
Publisher: North Holland
ISBN:
Category : Mathematics
Languages : en
Pages : 376
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Book Description
Statistical methods based on models with latent variables play an important role in the analysis of multivariate data. The subject can be approached theoretically or in an empirical, pragmatic way. The statistical problem is to make inferences about the latent variables and the relationships between them. Errors-in-variables models, factor analysis and latent structure models are all examples of this approach. This volume presents a selection of invited and contributed papers which address the problems involved in developing a unifying statistical theory for latent variable models.
Author: David J. Bartholomew
Publisher: John Wiley & Sons
ISBN: 1119973708
Category : Mathematics
Languages : en
Pages : 241
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Book Description
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.
Author: Michael C. Edwards
Publisher: Routledge
ISBN: 1136699791
Category : Psychology
Languages : en
Pages : 298
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Book Description
This book presents recent developments in the theory and application of latent variable models (LVMs) by some of the most prominent researchers in the field. Topics covered involve a range of LVM frameworks including item response theory, structural equation modeling, factor analysis, and latent curve modeling, as well as various non-standard data structures and innovative applications. The book is divided into two sections, although several chapters cross these content boundaries. Part one focuses on complexities which involve the adaptation of latent variables models in research problems where real-world conditions do not match conventional assumptions. Chapters in this section cover issues such as analysis of dyadic data and complex survey data, as well as analysis of categorical variables. Part two of the book focuses on drawing real-world meaning from results obtained in LVMs. In this section there are chapters examining issues involving assessment of model fit, the nature of uncertainty in parameter estimates, inferences, and the nature of latent variables and individual differences. This book appeals to researchers and graduate students interested in the theory and application of latent variable models. As such, it serves as a supplementary reading in graduate level courses on latent variable models. Prerequisites include basic knowledge of latent variable models.
Author:
Publisher: Elsevier
ISBN: 0080471269
Category : Mathematics
Languages : en
Pages : 458
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Book Description
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.
Author: Kees van Montfort
Publisher: Springer Science & Business Media
ISBN: 3642117600
Category : Mathematics
Languages : en
Pages : 311
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Book Description
Since Charles Spearman published his seminal paper on factor analysis in 1904 and Karl Joresk ̈ og replaced the observed variables in an econometric structural equation model by latent factors in 1970, causal modelling by means of latent variables has become the standard in the social and behavioural sciences. Indeed, the central va- ables that social and behavioural theories deal with, can hardly ever be identi?ed as observed variables. Statistical modelling has to take account of measurement - rors and invalidities in the observed variables and so address the underlying latent variables. Moreover, during the past decades it has been widely agreed on that serious causal modelling should be based on longitudinal data. It is especially in the ?eld of longitudinal research and analysis, including panel research, that progress has been made in recent years. Many comprehensive panel data sets as, for example, on human development and voting behaviour have become available for analysis. The number of publications based on longitudinal data has increased immensely. Papers with causal claims based on cross-sectional data only experience rejection just for that reason.
Author: David Dunson
Publisher: Springer Science & Business Media
ISBN: 0387767215
Category : Mathematics
Languages : en
Pages : 174
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Book Description
Random Effect and Latent Variable Model Selection In recent years, there has been a dramatic increase in the collection of multivariate and correlated data in a wide variety of ?elds. For example, it is now standard pr- tice to routinely collect many response variables on each individual in a study. The different variables may correspond to repeated measurements over time, to a battery of surrogates for one or more latent traits, or to multiple types of outcomes having an unknown dependence structure. Hierarchical models that incorporate subje- speci?c parameters are one of the most widely-used tools for analyzing multivariate and correlated data. Such subject-speci?c parameters are commonly referred to as random effects, latent variables or frailties. There are two modeling frameworks that have been particularly widely used as hierarchical generalizations of linear regression models. The ?rst is the linear mixed effects model (Laird and Ware , 1982) and the second is the structural equation model (Bollen , 1989). Linear mixed effects (LME) models extend linear regr- sion to incorporate two components, with the ?rst corresponding to ?xed effects describing the impact of predictors on the mean and the second to random effects characterizing the impact on the covariance. LMEs have also been increasingly used for function estimation. In implementing LME analyses, model selection problems are unavoidable. For example, there may be interest in comparing models with and without a predictor in the ?xed and/or random effects component.
Author: Maia Berkane
Publisher: Springer Science & Business Media
ISBN: 146121842X
Category : Mathematics
Languages : en
Pages : 285
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Book Description
This volume gathers refereed papers presented at the 1994 UCLA conference on "La tent Variable Modeling and Application to Causality. " The meeting was organized by the UCLA Interdivisional Program in Statistics with the purpose of bringing together a group of people who have done recent advanced work in this field. The papers in this volume are representative of a wide variety of disciplines in which the use of latent variable models is rapidly growing. The volume is divided into two broad sections. The first section covers Path Models and Causal Reasoning and the papers are innovations from contributors in disciplines not traditionally associated with behavioural sciences, (e. g. computer science with Judea Pearl and public health with James Robins). Also in this section are contri butions by Rod McDonald and Michael Sobel who have a more traditional approach to causal inference, generating from problems in behavioural sciences. The second section encompasses new approaches to questions of model selection with emphasis on factor analysis and time varying systems. Amemiya uses nonlinear factor analysis which has a higher order of complexity associated with the identifiability condi tions. Muthen studies longitudinal hierarchichal models with latent variables and treats the time vector as a variable rather than a level of hierarchy. Deleeuw extends exploratory factor analysis models by including time as a variable and allowing for discrete and ordi nal latent variables. Arminger looks at autoregressive structures and Bock treats factor analysis models for categorical data.
