Book contents
- Frontmatter
- Contents
- Preface to the second edition
- 1 Introduction
- 2 The concept of risk
- 3 Overview of count response models
- 4 Methods of estimation
- 5 Assessment of count models
- 6 Poisson regression
- 7 Overdispersion
- 8 Negative binomial regression
- 9 Negative binomial regression: modeling
- 10 Alternative variance parameterizations
- 11 Problems with zero counts
- 12 Censored and truncated count models
- 13 Handling endogeneity and latent class models
- 14 Count panel models
- 15 Bayesian negative binomial models
- Appendix A Constructing and interpreting interaction terms
- Appendix B Data sets, commands, functions
- References and further reading
- Index
11 - Problems with zero counts
Published online by Cambridge University Press: 05 June 2012
- Frontmatter
- Contents
- Preface to the second edition
- 1 Introduction
- 2 The concept of risk
- 3 Overview of count response models
- 4 Methods of estimation
- 5 Assessment of count models
- 6 Poisson regression
- 7 Overdispersion
- 8 Negative binomial regression
- 9 Negative binomial regression: modeling
- 10 Alternative variance parameterizations
- 11 Problems with zero counts
- 12 Censored and truncated count models
- 13 Handling endogeneity and latent class models
- 14 Count panel models
- 15 Bayesian negative binomial models
- Appendix A Constructing and interpreting interaction terms
- Appendix B Data sets, commands, functions
- References and further reading
- Index
Summary
I previously indicated that extended Poisson and negative binomial models have generally been developed to solve either a distributional or variance problem arising in the base Poisson and NB2 models. We shall discover later that some extended negative binomial models rely on the NB1 parameterization rather than on the NB2, e.g. fixed-effects negative binomial. But, except for a few models, most negative binomial models used in actual research are based on the NB2 model.
Changes to the negative binomial variance function were considered in the last chapter. In this chapter, we address the difficulties arising when the zero counts in Poisson and NB2 models differ considerably from model distributional assumptions. In the first section we address models that cannot have zero counts.
Zero-truncated count models
Many times we are asked to model count data that structurally exclude zero counts. Hospital length-of-stay data are an example of this type of data. When a patient first enters the hospital, the count begins upon registration, with the length of stay given as 1. There can be no 0 days – unless we are describing patients who do not enter the hospital. This latter situation describes a different type of model where there may be two generating processes – one, for example, for patients who may or may not enter the hospital, and another for patients who are admitted. This type of model will be discussed later.
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- Negative Binomial Regression , pp. 346 - 386Publisher: Cambridge University PressPrint publication year: 2011
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