Bayesian versus frequentist statistical inference for investi gating a one-off cancer cluster reported to a health department

Background The problem of silent multiple comparisons is one of the most difficult statistical problems faced by scientists. It is a particular problem for investigating a one-off cancer cluster reported to a health department because any one of hundreds, or possibly thousands, of neighbourhoods, schools, or workplaces could have reported a cluster, which could have been for any one of several types of cancer or any one of several time periods. Methods This paper contrasts the frequentist approach with a Bayesian approach for dealing with silent multiple comparisons in the context of a one-off cluster reported to a health department. Two published cluster investigations were re-analysed using the Dunn-Sidak method to adjust frequentist p-values and confidence intervals for silent multiple comparisons. Bayesian methods were based on the Gamma distribution. Results Bayesian analysis with non-informative priors produced results similar to the frequentist analysis, and suggested that both clusters represented a statistical excess. In the frequentist framework, the statistical significance of both clusters was extremely sensitive to the number of silent multiple comparisons, which can only ever be a subjective "guesstimate". The Bayesian approach is also subjective: whether there is an apparent statistical excess depends on the specified prior. Conclusion In cluster investigations, the frequentist approach is just as subjective as the Bayesian approach, but the Bayesian approach is less ambitious in that it treats the analysis as a synthesis of data and personal judgements (possibly poor ones), rather than objective reality. Bayesian analysis is (arguably) a useful tool to support complicated decision-making, because it makes the uncertainty associated with silent multiple comparisons explicit.

The failure of Australian legislation on indirect discrimination to detect the systemic racism which prevents Aboriginal people from fully participating in the workforce

Government figures put the current indigenous unemployment rate at around 23%, 3 times the unemployment rate for other Australians. This thesis aims to assess whether Australian indirect discrimination legislation can provide a remedy for one of the causes of indigenous unemployment - the systemic discrimination which can result from the mere operation of established procedures of recruitment and hiring. The impact of those practices on indigenous people is examined in the context of an analysis of anti-discrimination legislation and cases from all Australian jurisdictions from the time of the passing of the Racial Discrimination Act by the Commonwealth in 1975 to the present. The thesis finds a number of reasons why the legislation fails to provide equality of opportunity for indigenous people seeking to enter the workforce. In nearly all jurisdictions it is obscurely drafted, used mainly by educated middle class white women, and provides remedies which tend to be compensatory damages rather than change to recruitment policy. White dominance of the legal process has produced legislative and judicial definitions of "race" and "Aboriginality" which focus on biology rather than cultural difference. In the commissions and tribunals complaints of racial discrimination are often rejected on the grounds of being "vexatious" or "frivolous", not reaching the required standard of proof, or not showing a causal connection between race and the conduct complained of. In all jurisdictions the cornerstone of liability is whether a particular employment term, condition or practice is reasonable. The thesis evaluates the approaches taken by appellate courts, including the High Court, and concludes that there is a trend towards an interpretation of reasonableness which favours employer arguments such as economic rationalism, the maintenance of good industrial relations, managerial prerogative to hire and fire, and the protection of majority rights. The thesis recommends that separate, clearly drafted legislation should be passed to address indigenous disadvantage and that indigenous people should be involved in all stages of the process.

Classification of Airborne LIDAR Intensity Data Using Statistical Analysis and Hough Transform with Application to Power Line Corridors

Light Detection and Ranging (LIDAR) has great potential to assist vegetation management in power line corridors by providing more accurate geometric information of the power line assets and vegetation along the corridors. However, the development of algorithms for the automatic processing of LIDAR point cloud data, in particular for feature extraction and classification of raw point cloud data, is in still in its infancy. In this paper, we take advantage of LIDAR intensity and try to classify ground and non-ground points by statistically analyzing the skewness and kurtosis of the intensity data. Moreover, the Hough transform is employed to detected power lines from the filtered object points. The experimental results show the effectiveness of our methods and indicate that better results were obtained by using LIDAR intensity data than elevation data.