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5 - COM–Poisson Regression
Kimberly F. Sellers, Georgetown University, Washington DC
Book:

The Conway–Maxwell–Poisson Distribution

Published online:

02 March 2023

Print publication:

09 March 2023, pp 153-217
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Summary

This chapter introduces the Conway–Maxwell–Poisson regression model, along with adaptations of the model to account for zero-inflation, censoring, and data clustering. Section 5.1 motivates the consideration and development of the various COM–Poisson regressions. Section 5.2 introduces the regression model and discusses related issues including parameter estimation, hypothesis testing, and statistical computing in R. Section 5.3 advances that work to address excess zeroes, while Section 5.4 describes COM–Poisson models that incorporate repeated measures and longitudinal studies. Section 5.5 focuses attention on the R statistical packages and functionality associated with regression analysis that accommodates excess zeros and/or clustered data as described in the two previous sections. Section 5.6 considers a general additive model based on COM–Poisson. Finally, Section 5.7 informs readers of other statistical computing softwares that are also available to conduct COM–Poisson regression, discussing their associated functionality. The chapter concludes with discussion.

The teaching of statistical consulting
Part of
- Statistics
- Applications
K. G. Russell
Journal:

Journal of Applied Probability / Volume 38 / Issue A / 2001

Published online by Cambridge University Press:

14 July 2016, pp. 20-26

Print publication:

2001
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This paper considers how students might be taught statistical consulting. Desirable characteristics of a good consultant are described. While skill in statistical techniques is considered, it is just one of several areas of expertise that are discussed. Suggestions are made as to what should be included in a consulting course to equip a student with these characteristics.

Data structures—the problem of time series computing
John Henstridge
Journal:

Journal of Applied Probability / Volume 23 / Issue A / 1986

Published online by Cambridge University Press:

14 July 2016, pp. 407-411

Print publication:

1986
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The scope of statistical packages is largely defined by the data structures supported. It is claimed that none of the standard structures used in major packages is wholly suitable for time series data and this is the major reason why time series has not benefited from modern developments in statistical computing.