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Multivariate Generalized Linear Mixed Models Using R presents robust and methodologically sound models for analyzing large and complex data sets, enabling readers to answer increasingly complex research questions. The book applies the principles of modeling to longitudinal data from panel and related studies via the Sabre software package in R. A Unified Framework for a Broad Class of Models The authors first discuss members of the family of generalized linear models, gradually adding complexity to the modeling framework by incorporating random effects. After reviewing the generalized linear model notation, they illustrate a range of random effects models, including three-level, multivariate, endpoint, event history, and state dependence models. They estimate the multivariate generalized linear mixed models (MGLMMs) using either standard or adaptive Gaussian quadrature. The authors also compare two-level fixed and random effects linear models. The appendices contain additional information on quadrature, model estimation, and endogenous variables, along with SabreR commands and examples. Improve Your Longitudinal StudyIn medical and social science research, MGLMMs help disentangle state dependence from incidental parameters. Focusing on these sophisticated data analysis techniques, this book explains the statistical theory and modeling involved in longitudinal studies. Many examples throughout the text illustrate the analysis of real-world data sets. Exercises, solutions, and other material are available on a supporting website.

Ecologists are making increasing use of computer methods in analyzing ecological data on plant and animal communities. Ecological problems naturally involve numerous variables and numerous individuals or samples. Multivariate techniques permit the summary of large, complex sets of data and provide the means to tackle many problems that cannot be investigated experimentally because of practical restraints. Ecologists are thus enabled to group similar species and similar sample sites together, and to generate hypotheses about environmental and historical factors that affect the communities. This timely book presents a full critical description of three methodologies - direct gradient analysis, ordination, and classification - from both theoretical and practical viewpoints. Both traditional and new methods are presented. Using a wide range of illustrative examples, Hugh Gauch provides an up-to-date synthesis of this field, which will be of interest to advanced students and ecologists. These mathematical tools are also used in a wide variety of other areas, from natural resource management and agronomy to the social and political sciences.

Multivariate Analysis deals with observations on more than one variable where there is some inherent interdependence between the variables. With several texts already available in this area, one may very well enquire of the authors as to the need for yet another book. Most of the available books fall into two categories, either theoretical or data analytic. The present book not only combines the two approaches but it also has been guided by the need to give suitable matter for the beginner as well as illustrating some deeper aspects of the subject for the research worker. Practical examples are kept to the forefront and, wherever feasible, each technique is motivated by such an example.

MULTIVARIATE STATISTICAL METHODS fills a void in the marketplace, striking a crucial balance between the technical information and real-world applications of multivariate statistics. Donald Morrison has taught this course at the Wharton School for 33 years, and he has integrated his demonstrably successful teaching style into this textbook.

This long-anticipated textbook is currently the most up-to-date resource on chemometrics and Multivariate Data Analysis (MVA). The field of chemometrics is the application of multivariate data analysis methodology to solve chemistry-based problems. It explains not only how to understand experimental outputs, but also to put this newfound knowledge into use for deeper scientific understanding or business gains.The authors’ extensive practical experience from science, technology and industry is combined with the newest software on the market to present a unique didactic scope of MVA with full understanding of all necessary underlying principles. New topics such as PAT (Process Analytical Technology), QbD (Quality by Design) and TOS (Theory of Sampling), are covered in this 6th Edition. In addition to detailed explanations, all chapters have many illustrative examples for a better understanding of the concepts - and how they work in practice.All examples and case stories have been updated from previous versions of the book. Both specific and all-encompassing, both deeply theoretical and grounded in modern application, accessible but advanced, this book serves as a fantastic teaching and reference tool for understanding the field of chemometrics."An excellent book to introduce chemometrics in a smooth and user-friendly way, both from a theoretical as well as a practical point of view. It is certainly good supporting material for chemometric teaching at undergraduate and graduate levels in chemistry and assuredly in many related scientific fields, and a very good self-learning basis for anyone wanting to use chemometric methods in industry or academia."Anna de Juan, Universitat de Barcelona and Romà Tauler, IDAEA-CSIC

Book by C. Radhakrishna Rao, William M. Stiteler

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WILEY-INTERSCIENCE PAPERBACK SERIES 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. "In recent years many monographs have been published on specialized aspects of multivariate data-analysis–on cluster analysis, multidimensional scaling, correspondence analysis, developments of discriminant analysis, graphical methods, classification, and so on. This book is an attempt to review these newer methods together with the classical theory. . . . This one merits two cheers." –J. C. Gower, Department of Statistics Rothamsted Experimental Station, Harpenden, U.K. Review in Biometrics, June 1987 Multivariate Observations is a comprehensive sourcebook that treats data-oriented techniques as well as classical methods. Emphasis is on principles rather than mathematical detail, and coverage ranges from the practical problems of graphically representing high-dimensional data to the theoretical problems relating to matrices of random variables. Each chapter serves as a self-contained survey of a specific topic. The book includes many numerical examples and over 1,100 references.

Data analysis is a vital part of science today, and in assessing quality, multivariate analysis is often necessary in order to avoid loss of essential information. Martens provides a powerful and versatile methodology that enables researchers to design their investigations and analyse data effectively and safely, without the need for formal statistical training. * Offers an introductory explanation of multivariate analysis by graphical 'soft modelling' * Minimises mathematics, providing all technical details in the appendix * Presents itself in an accessible style with cartoons, self-assessment questions and a wide range of practical examples * Demonstrates the methodology for various types of quality assessment, ranging from human quality perception via industrial quality monitoring to environmental quality and its molecular basis All data sets available FREE online on "Chemometrics World" (http://www.wiley.co.uk/wileychi/chemometrics)

Pp. 294 5th Edition No-pa16apr2015-kap

The basis for much of medical public health practice comes from epidemiological research. This text describes current statistical tools that are used to analyze the association between possible risk factors and the actual risk of disease. Beginning with a broad conceptual framework on the disease process, it describes commonly used techniques for analyzing proportions and disease rates. These are then extended to model fitting, and the common threads of logic that bind the two analytic strategies together are revealed. Each chapter provides a descriptive rationale for the method, a worked example using data from a published study, and an exercise that allows the reader to practice the technique. Each chapter also includes an appendix that provides further details on the theoretical underpinnings of the method. Among the topics covered are Mantel-Haenszel methods, rates, survival analysis, logistic regression, and generalized linear models. Methods for incorporating aspects of study design, such as matching, into the analysis are discussed, and guidance is given for determining the power or the sample size requirements of a study. This text will give readers a foundation in applied statistics and the concepts of model fitting to develop skills in the analysis of epidemiological data.

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Provides state-of-the-art coverage for the researcher confronted with designing and executing a simulation study using continuous multivariate distributions. Concise writing style makes the book accessible to a wide audience. Well-known multivariate distributions are described, emphasizing a few representative cases from each distribution. Coverage includes Pearson Types II and VII elliptically contoured distributions, Khintchine distributions, and the unifying class for the Burr, Pareto, and logistic distributions. Extensively illustrated--the figures are unique, attractive, and reveal very nicely what distributions ``look like.'' Contains an extensive and up-to-date bibliography culled from journals in statistics, operations research, mathematics, and computer science.

For graduate and upper-level undergraduate marketing research courses. For over 30 years,  Multivariate Data Analysis  has provided readers with the information they need to understand and apply multivariate data analysis.  Hair et. al provides an applications-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to readers how to understand and make use of the results of specific statistical techniques. In this Seventh Edition, the organization of the chapters has been greatly simplified. New chapters have been added on structural equations modeling, and all sections have been updated to reflect advances in technology, capability, and mathematical techniques.

This classic multivariate statistics book has become the introduction of choice for researchers and students with a minimal mathematics background. In addition to providing a review of fundamental statistical methods, it provides a basic treatment of advanced computer-based multivariate analytical techniques; including correlation and regression analysis, analysis of variance, discriminant analysis, factor analysis, cluster analysis, and multidimensional scaling. The conceptual treatment emphasizes the rationales, applications, and interpretations, rather than the theoretical mathematical aspects of the most commonly used data analysis techniques in use today, closing the gap between spiraling technology and its intelligent application, providing students and researchers with a decided advantage in their future studies and careers. Its conceptual non-mathematical approach is especially helpful to students, researchers, and managers in the social and health sciences, education, and business. It is also the ideal springboard for those who will pursue advanced study of these powerful multivariate techniques.

For over 30 years, this text has provided students with the information they need to understand and apply multivariate data analysis. The eighth edition of Multivariate Data Analysis provides an updated perspective on the analysis of all types of data as well as introducing some new perspectives and techniques that are foundational in today's world of analytics. Multivariate Data Analysis serves as the perfect companion for graduate and postgraduate students undertaking statistical analysis for business degrees, providing an application-oriented introduction to multivariate analysis for the non-statistician. By reducing heavy statistical research into fundamental concepts, the text explains to students how to understand and make use of the results of specific statistical techniques.

Missing dust jacket; Pages can have notes/highlighting. Spine may show signs of wear. ~ ThriftBooks: Read More, Spend Less 2.1

Multivariate public key cryptosystems (MPKC) is a fast-developing area in cryptography. This book systematically presents the subject matter for a broad audience and is the first book to focus on this exciting new topic.

Book is in NEW condition. 1.54