Handbook of Mixture Analysis
Handbook of Mixture Analysis

Author: Sylvia Fruhwirth-Schnatter

Publisher: CRC Press

Published: 2019-01-04

Total Pages: 489

ISBN-13: 0429508867

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Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

Handbook of Mixture Analysis
Handbook of Mixture Analysis

Author: Sylvia Fruhwirth-Schnatter

Publisher: CRC Press

Published: 2019-01-04

Total Pages: 522

ISBN-13: 0429508247

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Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.

Finite Mixture Models
Finite Mixture Models

Author: Geoffrey McLachlan

Publisher: John Wiley & Sons

Published: 2004-03-22

Total Pages: 419

ISBN-13: 047165406X

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An up-to-date, comprehensive account of major issues in finitemixture modeling This volume provides an up-to-date account of the theory andapplications of modeling via finite mixture distributions. With anemphasis on the applications of mixture models in both mainstreamanalysis and other areas such as unsupervised pattern recognition,speech recognition, and medical imaging, the book describes theformulations of the finite mixture approach, details itsmethodology, discusses aspects of its implementation, andillustrates its application in many common statisticalcontexts. Major issues discussed in this book include identifiabilityproblems, actual fitting of finite mixtures through use of the EMalgorithm, properties of the maximum likelihood estimators soobtained, assessment of the number of components to be used in themixture, and the applicability of asymptotic theory in providing abasis for the solutions to some of these problems. The author alsoconsiders how the EM algorithm can be scaled to handle the fittingof mixture models to very large databases, as in data miningapplications. This comprehensive, practical guide: * Provides more than 800 references-40% published since 1995 * Includes an appendix listing available mixture software * Links statistical literature with machine learning and patternrecognition literature * Contains more than 100 helpful graphs, charts, and tables Finite Mixture Models is an important resource for both applied andtheoretical statisticians as well as for researchers in the manyareas in which finite mixture models can be used to analyze data.

Handbook of Cluster Analysis
Handbook of Cluster Analysis

Author: Christian Hennig

Publisher: CRC Press

Published: 2015-12-16

Total Pages: 753

ISBN-13: 1466551895

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Handbook of Cluster Analysis provides a comprehensive and unified account of the main research developments in cluster analysis. Written by active, distinguished researchers in this area, the book helps readers make informed choices of the most suitable clustering approach for their problem and make better use of existing cluster analysis tools.The

The Oxford Handbook of Quantitative Methods, Vol. 2: Statistical Analysis
The Oxford Handbook of Quantitative Methods, Vol. 2: Statistical Analysis

Author: Todd D. Little

Publisher: Oxford University Press

Published: 2013-02-01

Total Pages: 784

ISBN-13: 0199934908

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Research today demands the application of sophisticated and powerful research tools. Fulfilling this need, The Oxford Handbook of Quantitative Methods is the complete tool box to deliver the most valid and generalizable answers to todays complex research questions. It is a one-stop source for learning and reviewing current best-practices in quantitative methods as practiced in the social, behavioral, and educational sciences. Comprising two volumes, this handbook covers a wealth of topics related to quantitative research methods. It begins with essential philosophical and ethical issues related to science and quantitative research. It then addresses core measurement topics before delving into the design of studies. Principal issues related to modern estimation and mathematical modeling are also detailed. Topics in the handbook then segway into the realm of statistical inference and modeling with chapters dedicated to classical approaches as well as modern latent variable approaches. Numerous chapters associated with longitudinal data and more specialized techniques round out this broad selection of topics. Comprehensive, authoritative, and user-friendly, this two-volume set will be an indispensable resource for serious researchers across the social, behavioral, and educational sciences.

The SAGE Handbook of Quantitative Methodology for the Social Sciences
The SAGE Handbook of Quantitative Methodology for the Social Sciences

Author: David Kaplan

Publisher: SAGE

Published: 2004-06-21

Total Pages: 532

ISBN-13: 9780761923596

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Quantitative methodology is a highly specialized field, and as with any highly specialized field, working through idiosyncratic language can be very difficult made even more so when concepts are conveyed in the language of mathematics and statistics. The Sage Handbook of Quantitative Methodology for the Social Sciences was conceived as a way of introducing applied statisticians, empirical researchers, and graduate students to the broad array of state-of-the-art quantitative methodologies in the social sciences. The contributing authors of the Handbook were asked to write about their areas of expertise in a way that would convey to the reader the utility of their respective methodologies. Relevance to real-world problems in the social sciences is an essential ingredient of each chapter. The Handbook consists of six sections comprising twenty-five chapters, from topics in scaling and measurement, to advances in statistical modelling methodologies, and finally to broad philosophical themes that transcend many of the quantitative methodologies covered in this handbook.

Handbook of Structural Equation Modeling
Handbook of Structural Equation Modeling

Author: Rick H. Hoyle

Publisher: Guilford Publications

Published: 2023-02-17

Total Pages: 801

ISBN-13: 1462544649

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"This accessible volume presents both the mechanics of structural equation modeling (SEM) and specific SEM strategies and applications. The editor, along with an international group of contributors, and editorial advisory board are leading methodologists who have organized the book to move from simpler material to more statistically complex modeling approaches. Sections cover the foundations of SEM; statistical underpinnings, from assumptions to model modifications; steps in implementation, from data preparation through writing the SEM report; and basic and advanced applications, including new and emerging topics in SEM. Each chapter provides conceptually oriented descriptions, fully explicated analyses, and engaging examples that reveal modeling possibilities for use with readers' data. Many of the chapters also include access to data and syntax files at the companion website, allowing readers to try their hands at reproducing the authors' results"--

Handbook of Approximate Bayesian Computation
Handbook of Approximate Bayesian Computation

Author: Scott A. Sisson

Publisher: CRC Press

Published: 2018-09-03

Total Pages: 679

ISBN-13: 1439881510

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As the world becomes increasingly complex, so do the statistical models required to analyse the challenging problems ahead. For the very first time in a single volume, the Handbook of Approximate Bayesian Computation (ABC) presents an extensive overview of the theory, practice and application of ABC methods. These simple, but powerful statistical techniques, take Bayesian statistics beyond the need to specify overly simplified models, to the setting where the model is defined only as a process that generates data. This process can be arbitrarily complex, to the point where standard Bayesian techniques based on working with tractable likelihood functions would not be viable. ABC methods finesse the problem of model complexity within the Bayesian framework by exploiting modern computational power, thereby permitting approximate Bayesian analyses of models that would otherwise be impossible to implement. The Handbook of ABC provides illuminating insight into the world of Bayesian modelling for intractable models for both experts and newcomers alike. It is an essential reference book for anyone interested in learning about and implementing ABC techniques to analyse complex models in the modern world.

Qualitative Data Analysis
Qualitative Data Analysis

Author: Ian Dey

Publisher: Routledge

Published: 2003-09-02

Total Pages: 309

ISBN-13: 1134931468

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Qualitative Data Analysis shows that learning how to analyse qualitative data by computer can be fun. Written in a stimulating style, with examples drawn mainly from every day life and contemporary humour, it should appeal to a wide audience.

Qualitative Content Analysis in Practice
Qualitative Content Analysis in Practice

Author: Margrit Schreier

Publisher: SAGE

Published: 2012-02-21

Total Pages: 283

ISBN-13: 1446258750

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Qualitative content analysis is a powerful method for analyzing large amounts of qualitative data collected through interviews or focus groups. It is frequently employed by students, but introductory textbooks on content analysis have largely focused on the quantitative version of the method. In one of the first to focus on qualitative content analysis, Margrit Schreier takes students step-by step through: - creating a coding frame - segmenting the material - trying out the coding frame - evaluating the trial coding - carrying out the main coding - what comes after qualitative content analysis - making use of software when conducting qualitative content analysis. Each part of the process is described in detail and research examples are provided to illustrate each step. Frequently asked questions are answered, the most important points are summarized, and end of chapter questions provide an opportunity to revise these points. After reading the book, students are fully equiped to conduct their own qualitative content analysis. Designed for upper level undergraduate, MA, PhD students and researchers across the social sciences, this is essential reading for all those who want to use qualitative content analysis.