Discrete Latent Variable Models
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Discrete Latent Variable Models
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Author : Ton Heinen
language : en
Publisher:
Release Date : 1993
Discrete Latent Variable Models written by Ton Heinen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1993 with Factor analysis categories.
Nonparametric Bayesian Discrete Latent Variable Models For Unsupervised Learning
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Author :
language : en
Publisher:
Release Date : 2007
Nonparametric Bayesian Discrete Latent Variable Models For Unsupervised Learning written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Latent Class And Discrete Latent Trait Models
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Author : Ton Heinen
language : en
Publisher: SAGE Publications, Incorporated
Release Date : 1996-04-24
Latent Class And Discrete Latent Trait Models written by Ton Heinen and has been published by SAGE Publications, Incorporated this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996-04-24 with Mathematics categories.
In addition, he reviews log-linear models, latent trait models, and a number of restricted latent class models in detail as well as for the estimation of parameters for these models.
Latent Variable Models For Discrete Longitudinal Data With Measurement Error
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Author : Keith Humphreys
language : en
Publisher:
Release Date : 1996
Latent Variable Models For Discrete Longitudinal Data With Measurement Error written by Keith Humphreys and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1996 with categories.
Generalized Latent Variable Modeling
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Author : Anders Skrondal
language : en
Publisher: CRC Press
Release Date : 2004-05-11
Generalized Latent Variable Modeling written by Anders Skrondal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-11 with Mathematics categories.
This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models, item response or factor models, latent class or finite mixture models, and structural equation models. Following a gentle introduction to latent variable modeling, the authors clearly explain and contrast a wi
Latent Variable Modeling And Applications To Causality
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Author : Maia Berkane
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Latent Variable Modeling And Applications To Causality written by Maia Berkane and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
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.
A Deterministic Inference Framework For Discrete Nonparametric Latent Variable Models
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Author : Yordan Raykov
language : en
Publisher:
Release Date : 2017
A Deterministic Inference Framework For Discrete Nonparametric Latent Variable Models written by Yordan Raykov and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017 with categories.
Integrating Latent Variables In Discrete Choice Models
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Author : Dirk Temme
language : en
Publisher:
Release Date : 2007
Integrating Latent Variables In Discrete Choice Models written by Dirk Temme and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
Latent Variable Models
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Author : John C. Loehlin
language : en
Publisher: Psychology Press
Release Date : 2004-05-20
Latent Variable Models written by John C. Loehlin and has been published by Psychology Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2004-05-20 with Business & Economics categories.
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.
Prediction Of Latent Variables In A Mixture Of Structural Equation Models With An Application To The Discrepancy Between Survey And Register Data
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Author : Erik Meijer
language : en
Publisher:
Release Date : 2008
Prediction Of Latent Variables In A Mixture Of Structural Equation Models With An Application To The Discrepancy Between Survey And Register Data written by Erik Meijer and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008 with categories.
The authors study the prediction of latent variables in a finite mixture of linear structural equation models. The latent variables can be viewed as well-defined variables measured with error or as theoretical constructs that cannot be measured objectively, but for which proxies are observed. The finite mixture component may serve different purposes: it can denote an unobserved segmentation in subpopulations such as market segments, or it can be used as a nonparametric way to estimate an unknown distribution. In the first interpretation, it forms an additional discrete latent variable in an otherwise continuous latent variable model. Different criteria can be employed to derive 'optimal' predictors of the latent variables, leading to a taxonomy of possible predictors. The authors derive the theoretical properties of these predictors. Special attention is given to a mixture that includes components with degenerate distributions. They then apply the theory to the optimal estimation of individual earnings when two independent observations are available: one from survey data and one from register data. The discrete components of the model represent observations with or without measurement error, and with either a correct match or a mismatch between the two data sources.