I know of a paper where people split up the process in begin zero or positive (binomial), and the value of the process given that it is positive (Poisson). In fact you're working with a composite pdf, two spatial processes that have to be merged later on. The idea is attractive, but not very easy. If you want the title of the paper, email me. -- Edzer
Brian R Gray wrote:
you could modify the suggested approach by using a generalization of the Poisson, the neg binomial assumption you mention. most stat software allows negative binomial regression. in this case, the variance component of the Chi-squared resids may be better approximated (than under the Poisson assumption). as an aside, you may have a zillion zeroes with your fisheries data. such data may be handled moderately well by the neg bin assumption you mention. however, they may better be handled under the assumption that some portion of the zeroes are structural (ie *can't* generate a positive count) rather than stochastic. I haven't seen spatial corr assessed under these assumptions in the published lit. regardless, such "zero inflated" models are often considerably more complicated and may not suit your purposes. brian
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