Categorical data arise often in many fields, including biometrics, economics, management, Jeffrey S. Simonoff is Professor of Statistics at New York University. Request PDF on ResearchGate | Analyzing Categorical Data | Introduction.- Gaussian-Based Jeffrey S. Simonoff at New York University. Jeffrey S. Simonoff . Download Citation on ResearchGate | On Feb 1, , Stanley Wasserman and others published Analyzing Categorical Data. Jeffrey S. Simonoff }.

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Tesi di laurea Tesi di laurea Pubblica la tua tesi Guide per tesi e cv Come scrivere una tesi. Scegli il punto di consegna e ritira quando vuoi Scopri come. Springer Texts in Statistics Edizione: This book provides an introduction to the analysis of such data.

The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and tables with ordered categories; and regression models for two-category binary and multiple-category target variables, such as logistic and proportional odds models.


Analyzing Categorical Data

More than exercises are provided, many also based on recent subject area literature. Data sets and computer code are available at a web site devoted to the text. Adopters of this book may request a solutions manual from: The examples are superb.

Student reactions in a class I taught from this text were uniformly positive, particularly because of the examples and exercises. I liked this book for this reason, and recommend it to you for pedagogical purposes. The examples motivate the theory and also illustrate nuances of data analytical procedures.

The book also incorporates several newer methods for analyzing categorical data, including zero-inflated Poisson models, robust analysis of binomial and poisson models, sandwich estimators, multinomial smoothing, ordinal agreement tables There is much to learn from this analyzinv.

Aside from the ordinary materials such as association diagrams, Mantel-Haenszel estimators, or overdispersion, the reader will also find some less-often presented but interesting and stimulating topics It is of great help to data analysts, practitioners and researchers who deal with categorical data and need to get a necessary insight into the methods of analysis as well as practical guidelines for solving problems.

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Analyzing Categorical Data – Jeffrey S. Simonoff – Google Books

Independent Random Sampling Methods. Angewandte Zeitreihenanalyse mit R. Prodotto non disponibile Editore: Informazioni Lavora con noi.