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Latent Variable Analysis and Signal Separation

This book constitutes the proceedings of the 13th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICA 2017;

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Latent Variable Analysis and Signal Separation

This book constitutes the proceedings of the 12th International Conference on Latent Variable Analysis and Signal Separation, LVA/ICS 2015;

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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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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;

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Current Topics in the Theory and Application of Latent Variable Models

analysis, and latent curve modeling, as well as various non-standard data structures and innovative applications. The book is divided into two;

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Current Topics in the Theory and Application of Latent Variable Models

analysis, and latent curve modeling, as well as various non-standard data structures and innovative applications. The book is divided into two;

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Latent Variable Models

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing;

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Latent Variable Models

Latent Variable Models: An Introduction to Factor, Path, and Structural Equation Analysis introduces latent variable models by utilizing;

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Latent Variable and Latent Structure Models

-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two;

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Latent Variable and Latent Structure Models

-group analysis, and model testing. New methodological topics are illustrated with real applications. The material presented brings together two;

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Confirmatory Factor Analysi For Aplie Re

, bifactor analysis, measurement invariance evaluation with categorical indicators, and a new method for scaling latent variables. *Utilizes the;

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Signals and Systems

System and Signal Analysis, 3/e bridges the gap between simple linear circuit analysis and the study of larger, more complex systems. It;

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Advances in Latent Variable Mixture Models

latent variable mixture models before the volume moves to more specific constellations of topics. Part I, Multilevel and Longitudinal Systems;

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Generalized Latent Variable Modeling

This book unifies and extends latent variable models, including multilevel or generalized linear mixed models, longitudinal or panel models;

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Segmentation and Separation of Overlapped Latent Fingerprints

This Springerbrief presents an overview of problems and technologies behind segmentation and separation of overlapped latent fingerprints;

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Advances in Latent Variable Mixture Models

latent variable mixture models before the volume moves to more specific constellations of topics. Part I, Multilevel and Longitudinal Systems;

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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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Statistical Methods for Categorical Data Analysis

Intended for graduate students, this textbook introduces methods and models for the analysis of categorical data and their applications;

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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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Latent Markov Models for Longitudinal Data

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data;

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Latent Markov Models for Longitudinal Data

Drawing on the authors' extensive research in the analysis of categorical longitudinal data, Latent Markov Models for Longitudinal Data;

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Adaptive Blind Signal And Image Processing

efficient algorithms for blind source separation, Independent, Principal, Minor Component Analysis, and Multichannel Blind Deconvolution (MBD) and;

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Latent Variable Modeling Using R

and/or advanced undergraduate courses on latent variable modeling, factor analysis, structural equation modeling, item response theory;

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Latent Variable Modeling Using R

This step-by-step guide is written for R and latent variable model (LVM) novices. Utilizing a path model approach and focusing on the;

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An Introduction to Latent Class Analysis

This book provides methods and applications of latent class analysis, and the following topics are taken up in the focus of discussion;

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Latent Variable Modeling with R

uses, examples, sample code and output, and an interpretation of the results. Each chapter features a detailed example including the analysis;

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