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• ## Re: AI-GEOSTATS: About gstat and binomial negative family data

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• sounds like you are describing a two-part or hurdle model. a possibly more attractive but complex approach (zero-inflated count distributions) postulates two
Message 1 of 8 , Dec 1 5:54 AM
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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
brgray@...
*****************************************************************

|---------+---------------------------->
| | "Edzer J. |
| | Pebesma" |
| | <e.pebesma@geog.u|
| | u.nl> |
| | |
| | 11/29/2003 06:35 |
| | AM |
| | |
|---------+---------------------------->
>--------------------------------------------------------------------------------------------------------------------------------------------------|
| |
| To: Brian R Gray <brgray@...> |
| cc: ai-geostats@..., Marcelo Alexandre Bruno <marcelo2lei@...> |
| 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
>
>****************************************************************
>
>

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• If you can handle writing (or get someone to write) MCMC code/bayesian work then take a look a this zero inflated poisson with spatial effects. Zero-Inflated
Message 2 of 8 , Dec 1 8:51 AM
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If you can handle writing (or get someone to write) MCMC code/bayesian
work then take a look a this zero inflated poisson with spatial effects.
Zero-Inflated Models with Application to Spatial Count Data
D.K.Agarwal,A.E.Gelfand and S.Citron-Pousty, Environmental and
Ecological Statistics 2002, vol 9, pp 341-355
Zero inflated poisson comes awfully close to a negative binomial, and
makes more "biological" sense (i.e. there are a lot more places with
zero counts and there is a process to account for them).
Thanks,
Steve

Brian R Gray wrote:

>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
>brgray@...
>*****************************************************************
>
>
>|---------+---------------------------->
>| | "Edzer J. |
>| | Pebesma" |
>| | <e.pebesma@geog.u|
>| | u.nl> |
>| | |
>| | 11/29/2003 06:35 |
>| | AM |
>| | |
>|---------+---------------------------->
> >--------------------------------------------------------------------------------------------------------------------------------------------------|
> | |
> | To: Brian R Gray <brgray@...> |
> | cc: ai-geostats@..., Marcelo Alexandre Bruno <marcelo2lei@...> |
> | 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
>
>
>>
>>****************************************************************
>>
>>
>>
>>
>
>
>
>
>
>
>
>
>--
>* To post a message to the list, send it to ai-geostats@...
>* As a general service to the users, please remember to post a summary of any useful responses to your questions.
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>
>

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• Here are the answers I had for my question: Thank you very much. Marta ... -- * To post a message to the list, send it to ai-geostats@unil.ch * As a general
Message 3 of 8 , Dec 2 8:50 AM
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Thank you very much.
Marta

>>Dear list members,
>>
>>
>>I would like to know if anyone has information or bibliography on
>>backtransformation of the variogram or the variogram model.
>>I have 2 ref. only (Armstrong and Guiblin et al. 1995).
>>Is this supose to give similar results to the log-normal kriging?
>>Could anyone point me bibliography for this, please....
>>
>>Thank you very much in advance,
>>any help would be appreciated,
>>Best wishes
>>Marta

>It depends on the transformation you do!
>See GSLIB- book (1992) for normal score transform
>see
>G. Mateu-Figueras et al: Normal in R+ vs lognormal in R., 2002 (iamg
>meeting, berlin september 2002)

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