Statistical Approaches Applied on a Greek Lip Cancer Dataset

Abstract

Having at our disposal a dataset of 186 lip cancer cases in Greece, we attempt to interpret them by applying four different statistical methods: Generalized Linear Model (GLM), Markov Chain Monte Carlo (MCMC), Cox proportional hazards model and Bayes factor. The likelihood score equations from GLM exerted estimators with bounded influence, so that the resulting estimators were robust against outliers while maintaining high efficiency in the absence of outliers. Batch means method of estimating the variance of the asymptotic normal distribution, used in MCMC, gave strong consistency when it was applied to our data. A Cox proportional hazards model done with a weighted expectation-maximization gave efficient parameter estimates. Finally, using Bayes factor to the prior distributions for the parameters in compared regression models was proved to be highly sensitive.