Seminarios/Unicamp/Estatistica

[Ronaldo Dias <dias@ime.unicamp.br>]

Caros(as) Colegas,

O depto. de Estatistica-IMECC-Unicamp terá os seguintes seminários esta 
semana.

dia 24/09 as 15hs, sala 221: Prof. Gauss Cordeiro.

*Power Series Generalized Linear Models *


dia 25/09 as 11hs, sala 221: Prof. Dani Gamerman.

*Transfer Functions in Dynamic Generalized Linear Models*


Os resumos seguem ao final desta email. Contamos mais uma vez com sua prsença. Abraços,

Ronaldo


----------------------------------------------Resumos------------------------------

*Power Series Generalized Linear Models*

   (Gauss M. Cordeiro, Marinho de Andrade e Mário de Castro)

We introduce in this talk a new class of discrete generalized
linear models to extend the binomial, Poisson and negative binomial
models to cope with count data. This class of models includes some
important models such as log-linear models, logit, probit and negative
binomial models, generalized Poisson and generalized binomial
regression models, among other models, which enables the fitting of a
wide range of models to count data. We derive an iterative process for
fitting these models by maximum likelihood and discuss inference on the 
parameters. The usefulness of the new class of models is illustrated with an application to a real data set.


*Transfer Functions in Dynamic Generalized Linear Models.*
Dani Gamerman

In a time series analysis it is sometimes necessary to assume that
the effect of a regressor does not have only immediate impact on
the mean response, but that its effects somehow propagate to
future times. We adopt, in the present work, transfer functions to
model such impacts, represented by structural blocks present in
dynamic generalized linear models. All the inference is carried
under the Bayesian paradigm. Two sources of difficulties emerge
for the analytical derivation of posterior distributions:
non-Gaussian nature of the response, associated to non-conjugate
priors and also non-linearity of the predictor on autoregressive
parameters present in transfer functions. The purpose of this work
is to produce full Bayesian inference on dynamic generalized
linear models with transfer functions, using Markov Chain Monte
Carlo methods to build samples of the posterior joint distribution
of the parameters involved in such models. Several transfer
structures are specified, associated to Poisson, Binomial and 
Gamma responses. Simulated data are analyzed under
the resulting models in order to assess their performance.
Finally, two applications to real data concerning environmental
sciences are made under different model formulations.

Joint work with Mariane B. Alves and Marco A. R. Ferreira



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fn:Ronaldo Dias
n:Dias;Ronaldo
org:University of Campinas;Department of Statistics
adr;quoted-printable:Cidade Universit=C3=A1ria - Bar=C3=A3o Geraldo  ;;Rua Sergio Buarque de Holanda, 651  ;Campinas;SP;13083-859 ;Brazil
email;internet:dias@ime.unicamp.br
title:Associate Professor
url:www.ime.unicamp.br/~dias
version:2.1
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