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1-9 of 9 results for Author: joseph m hilbe (sorted by Publication Date, showing all)
  1. Practical Guide to Logistic Regression

    By Joseph M. Hilbe

    With a special focus on the useful application of logistic regression, this book is designed to be concise and practical, with brief outlines of the methods and detailed worked examples to show their application. The book includes code in R, SAS and STATA in the book, enabling users of any of these...

    To Be Published July 15th 2015 by Chapman and Hall/CRC

  2. Logistic Regression Models, Second Edition

    By Joseph M. Hilbe

    Series: Chapman & Hall/CRC Texts in Statistical Science

    The book provides a comprehensive overview of logistic regression models with a particular emphasis on their practical application. It covers the basic derivation of logistic models, their interpretation, various goodness-of-fit tests, over dispersion and how it can be handled, how models can be...

    To Be Published June 30th 2015 by Chapman and Hall/CRC

  3. Quasi-Least Squares Regression

    By Justine Shults, Joseph M. Hilbe

    Series: Chapman & Hall/CRC Monographs on Statistics & Applied Probability

    Drawing on the authors’ substantial expertise in modeling longitudinal and clustered data, Quasi-Least Squares Regression provides a thorough treatment of quasi-least squares (QLS) regression—a computational approach for the estimation of correlation parameters within the framework of generalized...

    Published January 28th 2014 by Chapman and Hall/CRC

  4. Methods of Statistical Model Estimation

    By Joseph M. Hilbe, Andrew P. Robinson

    Methods of Statistical Model Estimation examines the most important and popular methods used to estimate parameters for statistical models and provide informative model summary statistics. Designed for R users, the book is also ideal for anyone wanting to better understand the algorithms used for...

    Published May 28th 2013 by Chapman and Hall/CRC

  5. Generalized Estimating Equations, Second Edition

    By James W. Hardin, Joseph M. Hilbe

    Generalized Estimating Equations, Second Edition updates the best-selling previous edition, which has been the standard text on the subject since it was published a decade ago. Combining theory and application, the text provides readers with a comprehensive discussion of GEE and related models....

    Published December 10th 2012 by Chapman and Hall/CRC

  6. Generalized Linear Models and Extensions, Third Edition

    By James W. Hardin, Joseph M. Hilbe

    This book presents a thorough examination of generalized linear model (GLM) estimation methods as well as the derivation of all major GLM families. Examined families include Gaussian, gamma, inverse Gaussian, binomial, Poisson, geometric, and negative binomial. The text also contains various models...

    Published June 4th 2012 by Stata Press

  7. Generalized Linear Models

    Theory and Applications

    By Joseph M. Hilbe, James W. Hardin

    Series: Chapman & Hall/CRC Interdisciplinary Statistics

    Generalized Linear Models: Theory and Applications provides a comprehensive, practical introduction to generalized linear models that covers all of the main models and methods of estimation. Worked examples of real data are backed up by implementation in a range of software packages, including R,...

    Published June 15th 2010 by Chapman and Hall/CRC

  8. Logistic Regression Models

    By Joseph M. Hilbe

    Series: Chapman & Hall/CRC Texts in Statistical Science

    Logistic Regression Models presents an overview of the full range of logistic models, including binary, proportional, ordered, partially ordered, and unordered categorical response regression procedures. Other topics discussed include panel, survey, skewed, penalized, and exact logistic models. The...

    Published May 11th 2009 by Chapman and Hall/CRC

  9. Generalized Estimating Equations

    By James W. Hardin, Joseph M. Hilbe

    Although powerful and flexible, the method of generalized linear models (GLM) is limited in its ability to accurately deal with longitudinal and clustered data. Developed specifically to accommodate these data types, the method of Generalized Estimating Equations (GEE) extends the GLM algorithm to...

    Published July 30th 2002 by Chapman and Hall/CRC

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