Skip to main content Accessibility help
×
  1. Home
  2. Books
  3. The Conway–Maxwell–Poisson Distribution
  • Cited by 14
      • Show more authors
      • You may already have access via personal or institutional login
    • Select format
    • Publisher:
      Cambridge University Press
      Publication date:
      March 2023
      March 2023
      ISBN:
      9781108646437
      9781108481106
      9781009667135
      DOI:
      https://doi.org/10.1017/9781108646437
      Dimensions:
      (235 x 155 mm)
      Weight & Pages:
      0.65kg, 356 Pages
      Dimensions:
      Weight & Pages:
      0.577kg, 356 Pages
      • Subjects:
      Economics, Econometrics and Mathematical Methods, General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
      Series:
      Institute of Mathematical Statistics Monographs (8)
    You may already have access via personal or institutional login
    • Selected: Digital
    Add to cart View cart Buy from Cambridge.org
    Subjects:
    Economics, Econometrics and Mathematical Methods, General Statistics and Probability, Statistical Theory and Methods, Statistics and Probability
    Series:
    Institute of Mathematical Statistics Monographs (8)
    Information

    Book description

    While the Poisson distribution is a classical statistical model for count data, the distributional model hinges on the constraining property that its mean equal its variance. This text instead introduces the Conway-Maxwell-Poisson distribution and motivates its use in developing flexible statistical methods based on its distributional form. This two-parameter model not only contains the Poisson distribution as a special case but, in its ability to account for data over- or under-dispersion, encompasses both the geometric and Bernoulli distributions. The resulting statistical methods serve in a multitude of ways, from an exploratory data analysis tool, to a flexible modeling impetus for varied statistical methods involving count data. The first comprehensive reference on the subject, this text contains numerous illustrative examples demonstrating R code and output. It is essential reading for academics in statistics and data science, as well as quantitative researchers and data analysts in economics, biostatistics and other applied disciplines.

    Reviews

    ‘This book will be a great resource for anyone interested in modeling or analyzing count data. It offers a comprehensive perspective on the Conway-Maxwell-Poisson distribution.'

    Somnath Datta - University of Florida

    ‘This book is a terrific one-stop-shop for ‘everything COM-Poisson', not only integrating theoretical knowledge around the Conway-Maxwell Poisson distribution and its uses, but also providing practical notes and tips for applying the various relevant R libraries. Researchers and practitioners modeling count data or developing tools for modeling count data should find this book highly useful.'

    Galit Shmueli - National Tsing Hua University

    Refine List

    Actions for selected content:

    Select all | Deselect all
    • View selected items
    • Export citations
    • Download PDF (zip)
    • Save to Kindle
    • Save to Dropbox
    • Save to Google Drive

    Save Search

    You can save your searches here and later view and run them again in "My saved searches".

    Please provide a title, maximum of 40 characters.
    Cancel
    ×

    Contents

    Contents
    • 1 - Introduction: Count Data Containing Dispersion
      pp 1-21
        • You have access
      • PDF
      • Export citation
    References

    Metrics

    Altmetric attention score

    Full text views

    Total number of HTML views: 0
    Total number of PDF views: 0 *
    Loading metrics...

    Book summary page views

    Total views: 0 *
    Loading metrics...

    * Views captured on Cambridge Core between #date#. This data will be updated every 24 hours.

    Usage data cannot currently be displayed.

    Accessibility standard: Unknown

    Accessibility compliance for the PDF of this book is currently unknown and may be updated in the future.