Dear list,

This is the first time I have this type of data.  I have count data
collected repeatedly from the same plot with multiple years (14 yrs) and
have found that proportion of 'zero' values are very high (average of
proportion is about 92 %, min: 53 %, max: 100 %).  Only one year has 53% of
zeros in the data and the rest of years have at least greater than 86% zero
values in the data set.

The objective of the study is to develop predictive models and validate
them, for example, using cross validation.

Variables collected are: year, insect count, longitude, latitude, soil
properties (x1...x4).

Since data have too many zero observations, I am thinking about using zero
inflated model to fit the data.  However, I am very new to this method.

My questions are:
1. Is it possible to use zero inflated model to fit data with about 90%
zeros?  I am wondering if zero proportion is too high to make any inference
using statistical methods.
2. If I can use zero inflated models, can I use either Poisson distribution
or negative binomial distribution?  Or both?
3. Do you have any good reference (paper and/or website) for good and 'easy'
tutorial for me to study?

I am wondering if I provided enough information or submitted it to correct
mailing list.  Please let me know if you have any comments and suggestions.
I would greatly appreciate that.

Thank you very much in advance!!!

Steve

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