[ABE-L] Lembrete: Seminário dia 02-08-2017 - Quarta-feira - 14 horas

[Clarice Garcia Borges Demetrio]

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On Tue, Aug 1, 2017 at 9:10 AM, Clarice Garcia Borges Demetrio <
clarice.demetrio em usp.br> wrote:

> Segue a divulgação do Seminário que será ministrado pelo Prof. Dr. John
> Hinde, no dia 02/08/2017, às 14 horas, no Departamento de Ciências
> Exatas, ESALQ/USP.
>
>
>
> *Over/Under-dispersion, zero-infation and Poisson-Tweedie models*
> John Hinde1, Clarice Demétrio2 and Wagner Bonat3
> 1School of Mathematics, Statistics and Applied Mathematics, National
> University
> of Ireland, Galway, Ireland
> 2ESALQ/USP, Piracicaba, Brazil
> 3Paran a Federal University, Curitiba , Brazil
> Email: john.hinde em nuigalway.ie
>
> *Abstract*: The standard distributions for the analysis of count and
> proportion data
> are the Poisson and binomial distributions. Frequently, in practice they
> are too
> restrictive in that the variability in the data is either signi cantly
> greater (overdis-
> persed) or less (underdispersed) than that implied by the models variance
> function.
> For the analysis of count data, Nelder and McCullagh (1989) says that
> overdisper-
> sion is the norm and not the exception and this has been well studied, see
> Hinde and
> Demétrio (1999) and many subsequent articles presenting a wide range of
> distribu-
> tions. Although less common, underdispersion can arise, typically from
> dependent
> responses. For instance, when there is competition between plants and
> animals this
> can induce negative correlation in temporal and spatial counting
> processes. Here we
> will also consider how underdispersion can occur as a result of features
> of the under-
> lying counting, or data collection, process. The range of distributions
> for modelling
> underdispersed count data is relatively limited, although models can be
> derived in
> speci c situations.
> A class of general models is presented based on Poisson-Tweedie factorial
> dispersion
> models with variance phi mu^p, where mu is the mean, phi and p are the
> dispersion and
> Tweedie power parameters, respectively. This class of models provides a
> flexible and
> comprehensive family including many standard discrete models. The family
> provides
> for modelling of overdispersed count data, including Neyman Type A,
> Polya-Aeppli,
> negative binomial, Poisson-inverse Gaussian and Hermite distributions, and
> can
> also accommodate zero-infation and underdispersion. For a general approach
> we
> consider an extended version of the Poisson-Tweedie model and discuss
> estimation
> of regression, dispersion and Tweedie power (variance function)
> parameters. A full
> description of the approach is given in Bonat et al. (2017).
>
> References
> Bonat, W., J rgensen, B, Kokonendji, C., Hinde, J. and Demétrio, C.G.B.
> (2017)
> Extended Poisson-Tweedie: properties and regression models for count data.
> Statistical Modelling
> (accepted)
>
> Hinde, J. and Demétrio, C.G.B. (1998) Overdispersion: Models and
> estimation.
> Computational Statistics and Data Analysis. 27, 151{170.
>
> McCullagh, P. and Nelder, J.A. (1989). Generalized Linear Models. Chapman
> and
> Hall.
>
>
>
>
>
>
>
>
>
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