Researchers: Assoc Prof Alan Branford (leader), Prof Jerzy Filar, Dr Gobert Lee, Ms Ghazaleh Mohammadian.

We live in an era dominated by global financial crises, climate change, floods, droughts, bushfires, global epidemics and many other stochastic phenomena. In addition, this is also an era where, thanks to computer, sensor and satellite technologies, data are collected on a scale unimaginable only a few years ago. These twin drivers of the need to understand and manage uncertainty on the one hand and to exploit valuable information stored in enormous data bases on the other hand, ensure that subjects of Stochastic Modelling and Statistics are more relevant than ever. Consequently, the work of Flinders' Statistics and Stochastic Modelling research group focusses on problems related to risk management, probability modeling, classification theory and analysis of survival data. Specific projects, currently being investigated are listed below.

Professional and community engagement

Alan Branford is President of the South Australian Branch of the Statistical Society of Australia Inc.

Local grade inflation and local proportion of withdrawals

Alan Branford and his collaborators Jonathan Kuhn and Aaron Warren from Purdue University North Central and Diane Maletta from University of Notre Dame have been investigating relationships between grade inflation and proportion of withdrawals, at a small public university in the US. The findings of these investigations will appear in the Research of Higher Education Journal, published by Academic and Business Research Institute (AABRI).

The study was based on the assumption that educational institutions must be accountable to communities in general and students in particular, and fair and consistent assessment is an important component of this. If assigned grades for one group of students are higher than grades for a similar group of students, then grade inflation is said to be localised to the first group relative to the second. Local grade inflation is essentially a form of favouritism; one group of students is favored over another group of students. Identifying the behavior of local grade inflation involves comparing local grade point averages (LGPAs) of the grade distributions of different groups of students. Local grade point averages were calculated from the grades of individual students. Grade distributions for 7,500 class sections from a small public Midwestern University, from fall 1998 to fall 2007, were collected and analysed. Statistically significant (p-value < 0.01) categorical explanatory variables for LGPAs were compared and contrasted with statistically significant categorical explanatory variables for local proportions of withdrawals (LPWs). Statistical analysis found clear evidence (p-value < 0.01) that both LGPA and LPW are significantly different for different explanatory variables such as courses and instructors, as well as subjects, departments and academic course levels, but not for instructor academic qualifications, gender, and job category, nor for academic year, academic semester and class time period. Moreover, the R^2 measures of fit of model to data for one-variable, two-variable and multi-variable LGPA-dependent analysis of variance models were mostly larger than for equivalent LPW-dependent one-variable, two-variable and multi-variable models.

Unlocking the grid: the future of the electricity distribution network

This project consists of work carried out under Australian Research Council Linkage Grant (Dr. J.Boland, Mr D. Bruce, Prof J. A. Filar, Dr T. Wigley). Project titled “Unlocking the grid: the future of the electricity distribution network”. Duration: 2009-2011.

Increases in electricity demand lack uniformity in time and space. Power networks across Australia need considerable reinvestment over the next decade or so. This research models optimum placement of nodes within existing networks to best fit future spatial structure of demand. Significant benefits can be gained through a scientific design of the planned grid architecture, enabling timely and efficient incorporation of new wind or solar farms, and a carbon emission efficient and cost effective electricity grid management strategy.

Strategic integration of renewable energy systems into the electricity grid

This project consists of work carried out under Australian Research Council (Discovery Grant) ( Dr J. Boland and Prof. J.A. Filar). Project titled “Strategic integration of renewable energy systems into the electricity grid”. Duration: 2009-2011.

Electricity generation companies are using renewable energy systems to lower greenhouse gas emissions (GHG), but without determining which mixture of technologies is most beneficial. We exploit advanced stochastic modelling and dynamic optimisation techniques to structure the integration of renewable energy systems into the electricity supply system, to provide the greatest reduction in GHGs, at least cost, while maintaining supply strictures set by the National Electricity Market Management Company. Our methods aim to determine the mix of resources needed to schedule this integration to cater for the stochastic nature of both electricity demand and renewable energy resources while meeting mandatory targets.