Abstract
A general method for the analysis of ecological count data with extra zeros is presented using a Markov birth process representation of discrete distributions. The method uses a nonparametric formulation of the birth process to model the residual variation and therefore allows the data to play a greater role in determining an appropriate distribution. This enables a more critical assessment of covariate effects and more accurate predictions to be made. The approach is also presented as a useful diagnostic tool for suggesting appropriate parametric models or verifying standard models. As an illustrative example, data describing abundance of a species of possum from the montane ash forests of the central highlands of Victoria, southeast Australia, is considered.
Original language | English |
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Pages (from-to) | 324-334 |
Number of pages | 11 |
Journal | Journal of Agricultural, Biological and Environmental Statistics |
Volume | 7 |
Issue number | 3 |
DOIs | |
Publication status | Published - 1 Sept 2002 |
Keywords
- penalized likelihood
- covariate effects
- prediction
- extended Poisson process model