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Regression Analysis of Count Data

Regression Analysis of Count Data

Regression Analysis of Count Data

A. Colin Cameron, University of California, Davis
Pravin K. Trivedi, Indiana University
December 2004
Adobe eBook Reader
9780511059834
$52.00
USD
Adobe eBook Reader

    Students in both the natural and social sciences often seek regression models to explain the frequency of events, such as visits to a doctor, auto accidents or job hiring. This analysis provides a comprehensive account of models and methods to interpret such data. The authors have conducted research in the field for nearly fifteen years and in this work combine theory and practice to make sophisticated methods of analysis accessible to practitioners working with widely different types of data and software. The treatment will be useful to researchers in areas such as applied statistics, econometrics, operations research, actuarial studies, demography, biostatistics, quantitatively-oriented sociology and political science. The book may be used as a reference work on count models or by students seeking an authoritative overview. The analysis is complemented by template programs available on the Internet through the authors' homepages.

    • Authors are the two leading researchers in the field
    • Count data is exploding area in econometrics and applied statistical areas in biology, demography, insurance, labor studies, sociology, and political science
    • Complemented by template programs on the internet through the authors' homepages

    Reviews & endorsements

    '… a timely account of much of the relevant literature in this field.' Ivonia Rebelo, Economica

    See more reviews

    Product details

    December 2004
    Adobe eBook Reader
    9780511059834
    0 pages
    0kg
    11 b/w illus. 37 tables
    This ISBN is for an eBook version which is distributed on our behalf by a third party.

    Table of Contents

    • 1. Introduction
    • 2. Model specification and estimation
    • 3. Basic count regression
    • 4. Generalized count regression
    • 5. Model evaluation and testing
    • 6. Empirical Illustrations
    • 7. Time series data
    • 8. Multivariate data
    • 9. Longitudinal data
    • 10. Measurement errors
    • 11. Non-random samples and simultaneity
    • 12. Flexible methods for counts
    • Notations and acronyms
    • Functions, Distributions and moments
    • Software
    • References.
      Authors
    • A. Colin Cameron , University of California, Davis
    • Pravin K. Trivedi , Indiana University