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Advances in Latent Variables

Conference Advances in Latent Variables. Methods, Models and Applications , held at the Department of Economics and Management of the University;

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Advances in Latent Variables

Conference Advances in Latent Variables. Methods, Models and Applications , held at the Department of Economics and Management of the University;

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Advances in Latent Class Analysis

factor analysis model where both the measured variable indicators and underlying latent variables are categorical. Today, however, it rests;

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Longitudinal Research with Latent Variables

econometric structural equation model by latent factors in 1970, causal modelling by means of latent variables has become the standard in the social;

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Longitudinal Research with Latent Variables

econometric structural equation model by latent factors in 1970, causal modelling by means of latent variables has become the standard in the social;

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Semialgebraic Statistics and Latent Tree Models

Semialgebraic Statistics and Latent Tree Models explains how to analyze statistical models with hidden (latent) variables. It takes a;

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Semialgebraic Statistics and Latent Tree Models

Semialgebraic Statistics and Latent Tree Models explains how to analyze statistical models with hidden (latent) variables. It takes a;

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Current Topics in the Theory and Application of Latent Variable Models

nature of uncertainty in parameter estimates, inferences, and the nature of latent variables and individual differences. This book appeals to;

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Current Topics in the Theory and Application of Latent Variable Models

nature of uncertainty in parameter estimates, inferences, and the nature of latent variables and individual differences. This book appeals to;

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Latent Inhibition and Its Neural Substrates

latent inhibition, and shows how variables in the model can be mapped onto different brain regions and neurotransmitters. The result is a;

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New Developments in Categorical Data Analysis for the Social and Behavioral Sciences

methods use latent variables to explain the relationships among observed categorical variables. Latent class analysis yields the classification;

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Latent Variable Models and Factor Analysis

-linear models and models with mixed latent variables, both categorical and continuous. No prior acquaintance with latent variable modelling;

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Handbook of Latent Variable and Related Models

among observed and latent variables.- Covers a wide class of important models- Models and statistical methods described;

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Operationalizing Dynamic Pricing Models

input variables, such as latent demand and customer price sensitivity.;

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Structural Equation Modeling

allowing the evaluation of a series of simultaneous hypotheses about the impacts of latent and manifest variables on other variables, taking;

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Statistics and Neural Networks

principal component analysis, density networks and the use of multiple latent variables; and a substantial chapter outlining techniques and their;

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Confirmatory Factor Analysis

latent variables on the basis of substantive considerations rather than mathematical convenience.;

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Confirmatory Factor Analysis

authors explain, CFA is a theoretically informed statistical framework for linking multiple observed variables to latent variables that are not;

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Advances in Fingerprint Technology

, Advances in Fingerprint Technology , covers major developments in latent fingerprint processing, including physical, chemical, instrumental, and;

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Latent Curve Models

equation models (SEMs) with latent variables. While the authors discuss simple regression-based procedures that are useful in the early stages;

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Advances in Latent Variable Mixture Models

The current volume, Advances in Latent Variable Mixture Models, contains chapters by all of the speakers who participated in the 2006 CILVR;

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A Primer of Multivariate Statistics

Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable;

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A Primer of Multivariate Statistics

Statistics to provide a model of balance between how-to and why. This classic text covers multivariate techniques with a taste of latent variable;

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Advances in Latent Variable Mixture Models

The current volume, Advances in Latent Variable Mixture Models, contains chapters by all of the speakers who participated in the 2006 CILVR;

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Latent Variable Models

analysis using latent variables.;

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Latent Variable Models

analysis using latent variables.;

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The EM Algorithm and Related Statistical Models

data problems. The text covers current topics including statistical models with latent variables, as well as neural network models, and Markov;

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