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

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  • Steven Citron-Pousty
    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 1 of 8 , Dec 1, 2003
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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
      >
      >
      >>not suit your purposes. brian
      >>
      >>****************************************************************
      >>
      >>
      >>
      >>
      >
      >
      >
      >
      >
      >
      >
      >
      >--
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      >


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    • Marta Rufino
      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 2 of 8 , Dec 2, 2003
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        Here are the answers I had for my question:
        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


        Carme Hervada i Sala <carme.hervada@...> :
        >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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