Discrete Multivariate Analysis

Discrete Multivariate Analysis PDF

Author: Yvonne M. Bishop

Publisher: Springer Science & Business Media

Published: 2007-07-30

Total Pages: 558

ISBN-13: 0387728058

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“A welcome addition to multivariate analysis. The discussion is lucid and very leisurely, excellently illustrated with applications drawn from a wide variety of fields. 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 in 1974, this book is a reprint of a classic, still-valuable text.

Goodness-of-Fit Statistics for Discrete Multivariate Data

Goodness-of-Fit Statistics for Discrete Multivariate Data PDF

Author: Timothy R.C. Read

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 221

ISBN-13: 1461245788

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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 (1970), Haberman (1974, 1978, 1979), Bishop et al. (1975), Gokhale and Kullback (1978), Upton (1978), Fienberg (1980), Plackett (1981), Agresti (1984), Goodman (1984), and Freeman (1987). The objective of our book differs from those listed above. Rather than concentrating on model building, our intention is to describe and assess the goodness-of-fit statistics used in the model verification part of the inference process. Those books that emphasize model development tend to assume that the model can be tested with one of the traditional goodness-of-fit tests 2 2 (e.g., Pearson's X or the loglikelihood ratio G ) using a chi-squared critical value. However, it is well known that this can give a poor approximation in many circumstances. This book provides the reader with a unified analysis of the traditional goodness-of-fit tests, describing their behavior and relative merits as well as introducing some new test statistics. The power-divergence family of statistics (Cressie and Read, 1984) is used to link the traditional test statistics through a single real-valued parameter, and provides a way to consolidate and extend the current fragmented literature. As a by-product of our analysis, a new 2 2 statistic emerges "between" Pearson's X and the loglikelihood ratio G that has some valuable properties.

Discrete Data Analysis with R

Discrete Data Analysis with R PDF

Author: Michael Friendly

Publisher: CRC Press

Published: 2015-12-16

Total Pages: 700

ISBN-13: 1498725864

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An Applied Treatment of Modern Graphical Methods for Analyzing Categorical DataDiscrete 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 meth

The Statistical Analysis of Discrete Data

The Statistical Analysis of Discrete Data PDF

Author: Thomas J. Santner

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 381

ISBN-13: 1461210178

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The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data. The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prereq uisites are linear algebra and elementary advanced calculus. It assumes a basic statistics course which includes some decision theory, and knowledge of classical linear model theory for continuous response data. Problems are provided at the end of each chapter to give the reader an opportunity to ap ply the methods in the text, to explore extensions of the material covered, and to analyze data with discrete responses. In the text examples, and in the problems, we have sought to include interesting data sets from a wide variety of fields including political science, medicine, nuclear engineering, sociology, ecology, cancer research, library science, and biology. Although there are several texts available on discrete data analysis, we felt there was a need for a book which incorporated some of the myriad recent research advances. Our motivation was to introduce the subject by emphasizing its ties to the well-known theories of linear models, experi mental design, and regression diagnostics, as well as to describe alterna tive methodologies (Bayesian, smoothing, etc. ); the latter are based on the premise that external information is available. These overriding goals, to gether with our own experiences and biases, have governed our choice of topics.

Discrete Multivariate Analysis: Theory and Practice

Discrete Multivariate Analysis: Theory and Practice PDF

Author: Yvonne Millicent Mahala Bishop

Publisher: MIT Press (MA)

Published: 1975

Total Pages: 576

ISBN-13:

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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; Models for measuring change; Analysis of square tables: symmetry and marginal homogeneity; Model selection and assessing closeness of fit: practical aspects; Other methods for estimation and testing in cross-classifications; Measures of association and agreement; Pseudo-bayes estimates of cell probabilities; Sampling models for discrete data; Asymptotic methods.

Discrete Multivariate Analysis

Discrete Multivariate Analysis PDF

Author: Yvonne M. Bishop

Publisher: Springer Science & Business Media

Published: 2007-07-31

Total Pages: 559

ISBN-13: 0387728066

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“A welcome addition to multivariate analysis. The discussion is lucid and very leisurely, excellently illustrated with applications drawn from a wide variety of fields. 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 in 1974, this book is a reprint of a classic, still-valuable text.

Studies in Econometrics, Time Series, and Multivariate Statistics

Studies in Econometrics, Time Series, and Multivariate Statistics PDF

Author: Samuel Karlin

Publisher: Academic Press

Published: 2014-05-10

Total Pages: 591

ISBN-13: 1483268039

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Studies in Econometrics, Time Series, and Multivariate Statistics covers the theoretical and practical aspects of econometrics, social sciences, time series, and multivariate statistics. This book is organized into three parts encompassing 28 chapters. Part I contains studies on logit model, normal discriminant analysis, maximum likelihood estimation, abnormal selection bias, and regression analysis with a categorized explanatory variable. This part also deals with prediction-based tests for misspecification in nonlinear simultaneous systems and the identification in models with autoregressive errors. Part II highlights studies in time series, including time series analysis of error-correction models, time series model identification, linear random fields, segmentation of time series, and some basic asymptotic theory for linear processes in time series analysis. 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. Studies on Bayes procedures for combining independent F tests and the limit theorems on high dimensional spheres and Stiefel manifolds are included. This book will prove useful to statisticians, mathematicians, and advance mathematics students.

An Introduction to Applied Multivariate Analysis with R

An Introduction to Applied Multivariate Analysis with R PDF

Author: Brian Everitt

Publisher: Springer Science & Business Media

Published: 2011-04-23

Total Pages: 284

ISBN-13: 1441996508

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The majority of data sets collected by researchers in all disciplines are multivariate, meaning that several measurements, observations, or recordings are taken on each of the units in the data set. These units might be human subjects, archaeological artifacts, countries, or a vast variety of other things. In a few cases, it may be sensible to isolate each variable and study it separately, but in most instances all the variables need to be examined simultaneously in order to fully grasp the structure and key features of the data. For this purpose, one or another method of multivariate analysis might be helpful, and it is with such methods that this book is largely concerned. Multivariate analysis includes methods both for describing and exploring such data and for making formal inferences about them. The aim of all the techniques is, in general sense, to display or extract the signal in the data in the presence of noise and to find out what the data show us in the midst of their apparent chaos. An Introduction to Applied Multivariate Analysis with R explores the correct application of these methods so as to extract as much information as possible from the data at hand, particularly as some type of graphical representation, via the R software. Throughout the book, the authors give many examples of R code used to apply the multivariate techniques to multivariate data.

Introduction to Multivariate Analysis

Introduction to Multivariate Analysis PDF

Author: Chris Chatfield

Publisher: CRC Press

Published: 1981-05-15

Total Pages: 262

ISBN-13: 9780412160400

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This book provides an introduction to the analysis of multivariate data.It describes multivariate probability distributions, the preliminary analysisof a large -scale set of data, princ iple component and factor analysis, traditional normal theory material, as well as multidimensional scaling andcluster analysis.Introduction to Multivariate Analysis provides a reasonable blend oftheory and practice. Enough theory is given to introduce the concepts andto make the topics mathematically interesting. In addition the authors discussthe use (and misuse) of the techniques in pra ctice and present appropriatereal-life examples from a variety of areas includ ing agricultural research, soc iology and crim inology. The book should be suitable both for researchworkers and as a text for students taking a course on multivariate analysi