sounds like you are describing a two-part or hurdle model. a possibly more
attractive but complex approach (zero-inflated count distributions)
postulates two sources of zeroes: structural and stochastic. this doesn't
require working with a zero-truncated count distribution. the downside is
that the process defining how zeroes are separated is latent. brian
****************************************************************
Brian Gray, Ph.D.
USGS Upper Midwest Environmental Sciences Center
2630 Fanta Reed Road, La Crosse, WI 54602
608-783-7550 ext 19 - Onalaska campus or
608-781-6234 - La Crosse campus
fax 608-783-8058
[EMAIL PROTECTED]
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|---------+---------------------------->
| | "Edzer J. |
| | Pebesma" |
| | <[EMAIL PROTECTED]|
| | u.nl> |
| | |
| | 11/29/2003 06:35 |
| | AM |
| | |
|---------+---------------------------->
>--------------------------------------------------------------------------------------------------------------------------------------------------|
|
|
| To: Brian R Gray <[EMAIL PROTECTED]>
|
| cc: [EMAIL PROTECTED], Marcelo Alexandre Bruno <[EMAIL PROTECTED]>
|
| Subject: Re: AI-GEOSTATS: About gstat and binomial negative family data
|
>--------------------------------------------------------------------------------------------------------------------------------------------------|
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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