Higher-Order Growth Curves and Mixture Modeling with Mplus: A Practical Guide, Wickrama Kandauda A. S., Lee Tae Kyoung, O`Neal Catherine Walker
Старое издание
Автор: Peter Schlattmann Название: Medical Applications of Finite Mixture Models ISBN: 3642088163 ISBN-13(EAN): 9783642088162 Издательство: Springer Рейтинг: Цена: 18167.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book shows how to model heterogeneity in medical research with covariate adjusted finite mixture models. The areas of application include epidemiology, gene expression data, disease mapping, meta-analysis, neurophysiology and pharmacology.
Автор: Tatarinova Tatiana, Schumitzky Alan Название: Nonlinear Mixture Models: A Bayesian Approach ISBN: 1848167563 ISBN-13(EAN): 9781848167568 Издательство: World Scientific Publishing Рейтинг: Цена: 14256.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Provides an introduction to the important subject of nonlinear mixture models from a Bayesian perspective. This title contains background material, a brief description of Markov chain theory, as well as novel algorithms and their applications.
Автор: Ivan Nagy; Evgenia Suzdaleva Название: Algorithms and Programs of Dynamic Mixture Estimation ISBN: 3319646702 ISBN-13(EAN): 9783319646701 Издательство: Springer Рейтинг: Цена: 7685.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This book provides a general theoretical background for constructing the recursive Bayesian estimation algorithms for mixture models.
Автор: Wickrama, Kandauda A.s. Lee, Tae Kyoung (university Of Georgia, Usa) O`neal, Catherine Walker (university Of Georgia, Usa) Lorenz, Frederick O. Название: Higher-order growth curves and mixture modeling with mplus ISBN: 0367711265 ISBN-13(EAN): 9780367711269 Издательство: Taylor&Francis Рейтинг: Цена: 8879.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: This practical introduction to second-order and growth mixture models using Mplus introduces simple and complex techniques through incremental steps.The authors extend latent growth curves to second-order growth curve and mixture models and then combine the two using normal and non-normal (e.g., categorical) data. To maximize understanding, each model is presented with basic structural equations, figures with associated syntax that highlight what the statistics mean, Mplus applications, and an interpretation of results. Examples from a variety of disciplines demonstrate the use of the models and exercises allow readers to test their understanding of the techniques. A comprehensive introduction to confirmatory factor analysis, latent growth curve modeling, and growth mixture modeling is provided so the book can be used by readers of various skill levels. The book’s datasets are available on the web.New to this edition:* Two new chapters providing a stepwise introduction and practical guide to the application of second-order growth curves and mixture models with categorical outcomes using the Mplus program. Complete with exercises, answer keys, and downloadable data files.* Updated illustrative examples using Mplus 8.0 include conceptual figures, Mplus program syntax, and an interpretation of results to show readers how to carry out the analyses with actual data.This text is ideal for use in graduate courses or workshops on advanced structural equation, multilevel, longitudinal or latent variable modeling, latent growth curve and mixture modeling, factor analysis, multivariate statistics, or advanced quantitative techniques (methods) across the social and behavioral sciences.
Автор: Jichuan Wang, Xiaoqian Wang Название: Structural Equation Modeling: Applications Using Mplus ISBN: 1119422701 ISBN-13(EAN): 9781119422709 Издательство: Wiley Рейтинг: Цена: 11714.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Investigating fundamental issues in intellectual property (IP) law, this comparative collection brings together leading authors from around the world to provide perspectives across regimes, jurisdictions, and professions. This timely volume will appeal to a wide international audience that includes scholars, practicing lawyers, judges, and graduate students.
Автор: Finch Название: Multilevel Modeling Using Mplus ISBN: 1498748244 ISBN-13(EAN): 9781498748247 Издательство: Taylor&Francis Рейтинг: Цена: 8726.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Designed primarily for upper level undergraduate and graduate level students taking a course in multilevel modelling and/or statistical modelling with a large multilevel modelling component. Presents the theory and practice of major multilevel modelling techniques using Mplus as the software tool.
Автор: Geiser, Christian Название: Longitudinal Structural Equation Modeling with Mplus ISBN: 1462538789 ISBN-13(EAN): 9781462538782 Издательство: Taylor&Francis Рейтинг: Цена: 8726.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: An in-depth guide to executing longitudinal confirmatory factor analysis (CFA) and structural equation modeling (SEM) in Mplus, this book uses latent state-trait (LST) theory as a unifying conceptual framework, including the relevant coefficients of consistency, occasion specificity, and reliability.
Автор: Byrne Название: Structural Equation Modeling with Mplus ISBN: 0805859861 ISBN-13(EAN): 9780805859867 Издательство: Taylor&Francis Рейтинг: Цена: 24499.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание:
Modeled after Barbara Byrne's other best-selling structural equation modeling (SEM) books, this practical guide reviews the basic concepts and applications of SEM using Mplus Versions 5 & 6. The author reviews SEM applications based on actual data taken from her own research. Using non-mathematical language, it is written for the novice SEM user. With each application chapter, the author "walks" the reader through all steps involved in testing the SEM model including:
an explanation of the issues addressed
illustrated and annotated testing of the hypothesized and post hoc models
explanation and interpretation of all Mplus input and output files
important caveats pertinent to the SEM application under study
a description of the data and reference upon which the model was based
the corresponding data and syntax files available at http: //www.psypress.com/sem-with-mplus/datasets .
The first two chapters introduce the fundamental concepts of SEM and important basics of the Mplus program. The remaining chapters focus on SEM applications and include a variety of SEM models presented within the context of three sections: Single-group analyses, Multiple-group analyses, and other important topics, the latter of which includes the multitrait-multimethod, latent growth curve, and multilevel models.
Intended for researchers, practitioners, and students who use SEM and Mplus, this book is an ideal resource for graduate level courses on SEM taught in psychology, education, business, and other social and health sciences and/or as a supplement for courses on applied statistics, multivariate statistics, intermediate or advanced statistics, and/or research design. Appropriate for those with limited exposure to SEM or Mplus, a prerequisite of basic statistics through regression analysis is recommended.
Описание: Using a tried-and-tested, five-step process, this book provides students with a reader-friendly introduction to the major types of structural equation models implemented in the Mplus framework.
Автор: Shelton John Название: Testing Lack of Fit in a Mixture Model ISBN: 0530006693 ISBN-13(EAN): 9780530006697 Издательство: Неизвестно Рейтинг: Цена: 17105.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Abstract: A common problem in modeling the response surface in most systems, and in particular in a mixture system, is that of detecting lack of fit, or inadequancy, of a fitted model of the form E(Y) = Xg, in comparison to a model of the form E{Y) = Xe, + X B postulated as the true model. One method for detecting lack of fit involves comparing the value of the response observed at certain locations in the factor space, called "check points," with the value of the response that the fitted model predicts at these same check points. The observations at the check points are used only for testing lack of fit and are not used in fitting the model. It is shown that under the usual assumptions of independent and normally distributed errors, the lack of fit test statistic which uses the data at the check points is an F statistic. When no lack of fit is present the statistic possesses a central F distribution, but in general, in the presence of lack of fit, the statistic possesses a doubly noncentral F distribution. The power of this F test depends on the location of the check points in the factor space through its noncentrality parameters. A method of selecting check points that maximize the power of the test for lack of fit through their influence on the numerator noncentrality parameter is developed. A second method for detecting lack of fit relies on replicated response observations. The residual sum of squares from the fitted model is partitioned into a pure error variation component and into a lack of fit variation component. Lack of fit is detected if the lack of fit variation is large in comparison to the pure error variation. This method can be generalized when "near neighbor" observations must be substituted for replicates. In this case, the test statistic (assuming independent and normally distributed errors) has a central F distribution when the fitted model is adequate and a doubly noncentral F distribution under lack of fit. The arrangement of near neighbors is seen to affect the testing procedure and its power. Dissertation Discovery Company and University of Florida are dedicated to making scholarly works more discoverable and accessible throughout the world. This dissertation, "Testing Lack of Fit in a Mixture Model" by John Thomas Shelton, was obtained from University of Florida and is being sold with permission from the author. A digital copy of this work may also be found in the university's institutional repository, IR@UF. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation.
Автор: B.K. Sinha; N.K. Mandal; Manisha Pal; P. Das Название: Optimal Mixture Experiments ISBN: 8132217853 ISBN-13(EAN): 9788132217855 Издательство: Springer Рейтинг: Цена: 13275.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Opening with the basics of regression designs, this book reviews linear and quadratic Scheffe mixture models, applies the Darroch-Waller three-component quadratic mixture model to the general q-component model, discusses non-standard mixture designs and more.
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