Improvement of Wald residual in generalized linear models / Melhoramento do resíduo de Wald em modelos lineares generalizados

AUTOR(ES)

Mariana Ragassi Urbano

DATA DE PUBLICAÇÃO

2008

RESUMO

The theory of generalized linear models is very used in statistics, not only for modeling data normally distributed, but in the modeling of data whose distribution belongs to the exponential family of distributions. Some examples are binomial, gamma and inverse Gaussian distribution, among others. After tting a model in order to check the adequacy of tting, diagnostic techniques are used. The properties of residuals in generalized linear models are not well known, and asymptotic results are the only recourse. This work aims to study the asymptotic properties of Wald residual, and to obtain corrections to make the distribution of the modied residuals closer to standard normal. An application of the corrections for Wald residuals was done to ve datasets. In two datasets the response variables were counts, and to model, was used the Poisson distribution. Other two datasets are provided from a completely randomized design with a continuous response, and to model, was used the normal distribution, and, in the last dataset the interest was to model the proportion and the binomial distribution was used. A Monte Carlo simulation, was performed showing that the distribution of the corrected Wald residuals, is more close to the standard normal distribution.

ASSUNTO(S)

modelos lineares generalizados asymptotic theory (statistical inference) método de monte carlo distribuição normal generalized linear models teoria assintótica (inferência estatística). normal distribution monte carlo method.

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