Browsing by Subject "Robust quasi-Poisson"

In practice, outlying observations are not uncommon in many study domains. Without knowing the underlying factors to the outliers, it is appealing to eliminate the outliers from the datasets. However, unless there are scientific justification, outlier elimination amounts to alteration of the datasets. Otherwise, heavy-tailed distributions should be adopted to model the larger-than-expected variabiltiy in an overdispersed dataset. The Poisson distribution is the standard model to model the variation in count data. However, the empirical variability in observed datsets is often larger than the amount expected by the Poisson. This leads to unreliable inferences when estimating the true effect sizes of covariates in regression modelling. It follows that the Negative Binomial distribution is often adopted as an alternative to deal with the overdispersed datasets. Nevertheless, it has been proven that both Poisson and Negative Binomial observation distributions are not robust against the outliers, in a sense that the outliers have non-negligible influence on the estimation of the covariate effect size. On the other hand, the scale mixture of quasi-Poisson distributions (called the robust quasi-Poisson model), which is constructed similarly to the construction of the Student's t-distribution, is a heavy-tailed alternative to the Poisson. It is proven to be robust against outliers. The thesis shows the theoretical evidence on the robustness of the 3 aforementioned models in a Bayesian framework. Lastly, the thesis considers 2 simulation experiments with different kinds of the outlier source -- process error and covariate measurement error, to compare the robustness between the Poisson, Negative Binomial and robust quasi-Poisson regression models in the Bayesian framework. The model robustness was assessed, in terms of the model ability to infer correctly the covariate effect size, in different combination of error probability and error variability. It was proven that the robust quasi-Poisson regression model was more robust than its counterparts because its breakdown point was relatively higher than the others, in both experiments.