Discrete Multivariate Analysis Theory and Practice by Yvonne M. Bishop

Cover of: Discrete Multivariate Analysis | Yvonne M. Bishop

Published by The MIT Press .

Written in English

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Subjects:

  • Probability & statistics,
  • Mathematics,
  • Science/Mathematics,
  • Probability & Statistics - Multivariate Analysis,
  • Mathematics / General,
  • General

Book details

The Physical Object
FormatPaperback
Number of Pages568
ID Numbers
Open LibraryOL9889736M
ISBN 100262520400
ISBN 109780262520409

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A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject." --D.R. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience.

Exposition is quite discursive, and the mathematical level, except in Chapters 12 is very by: A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject." --D.R. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience.

Exposition is quite discursive, and the mathematical level, except in Chapters 12 is very elementary. A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject." - D.R. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience.

"The book deals with discrete multivariate analysis in an effort to bring together in an organised way the extensive theory and practice existing in this field. It is organised in 14 chapters. is well addressed to readers from different background and different interests covering a wide range from graduate students in theoretical Price: $   Discrete Multivariate Analysis: Theory and Practice: Yvonne M.M.

Bishop Yvonne Bishop, Stephen E. Fienberg, Paul W. Holland No preview available - Common terms and phrases. Discrete Multivariate Distributions begins with a general overviewof the multivariate method in which the authors lay the basictheoretical groundwork for the discussions that follow.

For clarityand consistency, subsequent chapters follow a similar format,beginning with a concise historical account followed by adiscussion of properties and characteristics. The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two decades.

The develop­ ment ofappropriate models is the common theme of books such as Cox (), Haberman (,), Bishop et al. (), Gokhale and Kullback (), Upton (), Fienberg (), Plackett (), Agresti (), Goodman (), and Cited by: A good part of the book can be understood without very specialized statistical knowledge.

It is a most welcome contribution to an interesting and lively subject." --D.R. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience.

Exposition is quite discursive, and the mathematical level, except in Chapters 12 is very elementary.5/5(1). Discrete Multivariate Analysis: Theory and Practice. Analysis of square tables: symmetry and marginal homogeneity -- Model selection and assessing closeness of fit: practical aspects -- Other.

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It is a most welcome contribution to an interesting and lively subject." --D.R. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience. Exposition is quite discursive, and the mathematical level.

NYUSA), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden.

Compare book prices from overbooksellers. Find Discrete multivariate analysis: theory and practice () by Bishop, Yvonne M. Discrete Multivariate Distributions begins with a general overview of the multivariate method in which the authors lay the basic theoretical groundwork for the discussions that follow.

For clarity and consistency, subsequent chapters follow a similar format, beginning with a concise historical account followed by a discussion of properties and characteristics.

The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two decades. The develop­ ment ofappropriate models is the common theme of books such as Cox (), Haberman (,), Bishop et al.

(), Gokhale. Discrete multivariate analysis offers a systematic treatment of multivariate categorical data primarily, indeed almost entirely, within the framework of the so-called log-linear probability model. Discrete Multivariate Analysis: Theory and Practice by Bishop, Yvonne M.M., etc.

and a great selection of related books, art and collectibles available now at Perhaps "Applied Multivariate Data Analysis", 2nd edition, by Everitt, B.

and Dunn, G. (), published by Arnold. [Roger Johnson] Rencher's Methods of Multivariate Analysis is a great resource. I think a strong undergraduate student could grasp the material.

[Philip Yates]. I'm fond of Rencher's approach. ISBN: OCLC Number: Description: x, pages: illustrations ; 26 cm: Contents: Structural models for counted data --Maximum likelihood estimates for complete tables --Formal goodness of fit: summary statistics and model selection --Maximum likelihood estimation for incomplete tables --Estimating the size of a closed population.

Skip to main content. MENU. SearchCited by: Multivariate equilibrium distributions of different forms are defined in the discrete case.

An important aspect to be considered in modelling and analyzing multivariate data is the dependence relation that exists between the components. --D.R. Cox, Nature "Discrete Multivariate Analysis is an ambitious attempt to present log-linear models to a broad audience. Exposition is quite discursive, and the mathematical level, except in Chapters 12 is very elementary.

Part III contains papers on optimality properties in discrete multivariate analysis, Anderson’s probability inequality, and asymptotic distributions of test statistics. This part also presents the comparison of measures, multivariate majorization, and of experiments for some multivariate normal situations.

The discrete multivariate modeling methods that are the subject of this page are also known in the systems literature as "reconstructability analysis" (RA). RA overlaps significantly with the fields of logic design and machine learning and with log-linear statistical modeling and Bayesian papers.

7 Multivariate Analysis. Many datasets consist of several variables measured on the same set of subjects: patients, samples, or organisms. For instance, we may have biometric characteristics such as height, weight, age as well as clinical variables such as blood pressure, blood sugar, heart rate, and genetic data for, say, a thousand patients.

Book Description. An Applied Treatment of Modern Graphical Methods for Analyzing Categorical Data. Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data.

It explains how to use graphical methods for exploring data. Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed can be applied to other types of.

Discrete Multivariate Analysis: Theory and Practice. 3 2. A good part of the book can be understood without very specialized statistical knowledge. It is a most welcome contribution to an interesting and lively subject.” -- Nature Originally published inthis book is.

Structural Sensitivity in Econometric Models Edwin Kuh, John W. Neese and Peter Hollinger Provides a pathbreaking assessment of the worth of linear dynamic systems methods for probing the behavior of complex macroeconomic models.

Representing a major improvement upon the standard black box approach to analyzing economic model structure, it introduces the powerful concept of parameter. Yvonne Millicent Mahala Bishop (died ) was an American statistician.

She wrote a "classic" book on multivariate statistics, and made important studies of the health effects of anesthetics and air pollution. Later in her career, she became the Director of the Office of Statistical Standards in Alma mater: Harvard University.

a| "With a focus on models and tangible applications of probability from physics, computer science, and other related disciplines, this book successfully guides readers through fundamental coverage for enhanced understanding of the problems. Topical coverage includes: bivariate discrete random, continuous random, and stochastic independence-multivariate random variables; transformations of.

Discrete Multivariate Analysis: Theory and Practice, MIT Press. Reprinted by Springer-Verlag, New York (). Christensen, Ronald (). Log-Linear Models and Logistic Regression, 2nd Ed., This book has a much more theoretical form than the others listed and will be of.

The Statistical Analysis of Discrete Data by Thomas J. Santnerand Diane E. Duffy Springer Verlag, New York This is a graduate level introduction to the use of loglinear models to analyze discrete multivariate data and to the analysis of binary response data, including 2 X 2 tables, stratified 2 X 2 tables, and binary regression.

Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data - Ebook written by Michael Friendly, David Meyer.

Read this book using Google Play Books app on your PC, android, iOS devices. Download for offline reading, highlight, bookmark or take notes while you read Discrete Data Analysis with R: Visualization and Modeling Techniques for.

brief description of multivariate analysis for discrete random variables is included in another presentation (on statistical inference).

a major focus of the presentation is to cover material that is necessary to an understanding of multivariate time series analysis. continuous multivariate analysis requires a basic knowledge ofFile Size: KB. "discrete" groups, then use Cluster Analysis Multivariate Techniques If the research objective is to: PAssign entities to a specified number of groups to maximize within-group similarity or form composite clusters PAssign entities to groups and display relationships among groups as they form Non-hierarchical Cluster Analysis HierarchicalFile Size: KB.

MULTIVARIATE PROBABILITY DISTRIBUTIONS 3 Once the joint probability function has been determined for discrete random variables X 1 and X 2, calculating joint probabilities involving X 1 and X 2 is straightforward. Example 1. Roll a red die and a green die.

Let X 1 = number of dots on the red die X 2 = number of dots on the green die. Log-linear models for discrete data are (1) just as flexible as corresponding linear models for continuous data, (2) consistent with distributional assumptions appropriate for discrete variables, and (3) based on measures of relationship (bivariate or multivariate) that are meaningful for discrete variables (odds, odds ratios, and their Cited by: MANCOVA (Multivariate Analysis of Covariance) is used when you include both categorical/discrete (independent variable) and continuous (covariate) variables as predictors of the linear combination of two or more quantitative dependent variables.

As with ANOVA, the independent variables for a MANOVA are factors, and each factor has twoFile Size: 54KB. Multivariate analysis: discrete variables, correspondence models. description: Product Description: The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two develop­ ment ofappropriate models is the common theme of books such as Cox (), Haberman (,), Bishop et al.

(), Gokhale and Kullback (), Upton (). (source: Nielsen Book Data) Summary An Applied Treatment of Modern Graphical Methods for Analyzing Categorical Data Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data presents an applied treatment of modern methods for the analysis of categorical data, both discrete response data and frequency data.6 Multivariate Data Analysis For Dummies Multivariate data analysis is the investigation of many vari-ables, simultaneously, in order to understand the relation-ships that may exist between them.

MVA can be as simple as analysing two variables right up to millions. Multivariate analysis adds a File Size: KB.

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