Geoffrey J. McLachlan's Books

Analyzing Microarray Gene Expression Data

By: Geoffrey J. McLachlan,Kim-Anh Do,Christophe Ambroise

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Analyzing Microarray Gene Expression Data

By: Geoffrey J. McLachlan,Kim-Anh Do,Christophe Ambroise

A multi-discipline, hands-on guide to microarray analysis of biological processes Analyzing Microarray Gene Expression Data provides a comprehensive review of available methodologies for the analysis of data derived from the latest DNA microarray technologies. Designed for biostatisticians entering the field of microarray analysis as well as biologists seeking to more effectively analyze their own experimental data, the text features a unique interdisciplinary approach and a combined academic and practical perspective that offers readers the most complete and applied coverage of the subject matter to date. Following a basic overview of the biological and technical principles behind microarray experimentation, the text provides a look at some of the most effective tools and procedures for achieving optimum reliability and reproducibility of research results, including: An in-depth account of the detection of genes that are differentially expressed across a number of classes of tissues Extensive coverage of both cluster analysis and discriminant analysis of microarray data and the growing applications of both methodologies A model-based approach to cluster analysis, with emphasis on the use of the EMMIX-GENE procedure for the clustering of tissue samples The latest data cleaning and normalization procedures The uses of microarray expression data for providing important prognostic information on the outcome of disease

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Page Count

366

Publisher

John Wiley & Sons

Published Date

ISBN 10

0471726125

ISBN 13

9780471726128

Finite Mixture Models

By: Geoffrey McLachlan,David Peel

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Finite Mixture Models

By: Geoffrey McLachlan,David Peel

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.

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419

Publisher

John Wiley & Sons

Published Date

ISBN 10

047165406X

ISBN 13

9780471654063

The EM Algorithm and Extensions

By: Geoffrey J. McLachlan,Thriyambakam Krishnan

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The EM Algorithm and Extensions

By: Geoffrey J. McLachlan,Thriyambakam Krishnan

The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.

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Page Count

399

Publisher

John Wiley & Sons

Published Date

ISBN 10

0470191600

ISBN 13

9780470191606

Discriminant Analysis and Statistical Pattern Recognition

By: Geoffrey J. McLachlan

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Discriminant Analysis and Statistical Pattern Recognition

By: Geoffrey J. McLachlan

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "For both applied and theoretical statisticians as well as investigators working in the many areas in which relevant use can be made of discriminant techniques, this monograph provides a modern, comprehensive, and systematic account of discriminant analysis, with the focus on the more recent advances in the field." –SciTech Book News ". . . a very useful source of information for any researcher working in discriminant analysis and pattern recognition." –Computational Statistics Discriminant Analysis and Statistical Pattern Recognition provides a systematic account of the subject. While the focus is on practical considerations, both theoretical and practical issues are explored. Among the advances covered are regularized discriminant analysis and bootstrap-based assessment of the performance of a sample-based discriminant rule, and extensions of discriminant analysis motivated by problems in statistical image analysis. The accompanying bibliography contains over 1,200 references.

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553

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John Wiley & Sons

Published Date

ISBN 10

0471725285

ISBN 13

9780471725282

Discriminant Analysis and Statistical Pattern Recognition

By: Geoffrey J. McLachlan

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Discriminant Analysis and Statistical Pattern Recognition

By: Geoffrey J. McLachlan

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553

Publisher

John Wiley & Sons

Published Date

ISBN 10

0471725285

ISBN 13

9780471725282

Finite Mixture Models

By: Geoffrey McLachlan,David Peel

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Finite Mixture Models

By: Geoffrey McLachlan,David Peel

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Page Count

419

Publisher

John Wiley & Sons

Published Date

ISBN 10

047165406X

ISBN 13

9780471654063

Book's by Geoffrey J. McLachlan

Analyzing Microarray Gene Expression Data

Analyzing Microarray Gene Expression Data

Finite Mixture Models

Finite Mixture Models

The EM Algorithm and Extensions

The EM Algorithm and Extensions

Discriminant Analysis and Statistical Pattern Recognition

Discriminant Analysis and Statistical Pattern Recognition

Discriminant Analysis and Statistical Pattern Recognition

Discriminant Analysis and Statistical Pattern Recognition

Finite Mixture Models

Finite Mixture Models

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