School of Computer and Mathematical Sciences
Faculty of Sciences, Engineering and Technology
Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.
It is well known that classical moment-based methods may not be reliable in the presence of skew and outliers. However, estimators based on quantiles are more robust in such instances and can provide more reliable inferences. Owing to advances in computing, quantile-based estimators have now become more accessible.
In light of these advancements, my research developments aim to contribute to the existing body of knowledge in Robust Statistics in the area of quantile-based methods in modeling and estimation. These new contributions include simulation studies, the construction of new measures, interactive web applications and computational packages with real data applications in this area of research. These measures and their estimates have applications in economics and the health sciences and address significant issues related to the broader scientific research community.
Year Citation 2021 Dedduwakumara, D. S., Prendergast, L. A., & Staudte, R. G. (2021). An efficient estimator of the parameters of the generalized lambda distribution. JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, 91(1), 197-215.
DOI Scopus3 WoS2
2021 Dedduwakumara, D. S., Prendergast, L. A., & Staudte, R. G. (2021). Some confidence intervals and insights for the proportion below the relative poverty line. SN Business & Economics, 1(10), 22 pages.
2020 Dedduwakumara, D. (2020). Contributions to Estimation and Modeling Using Quantiles. Bulletin of the Australian Mathematical Society, 103(3), 523-524.
2019 Dedduwakumara, D. S., Prendergast, L. A., & Robert, G. S. (2019). A simple and efficient method for finding the closest generalized lambda distribution to a specific model. Cogent Mathematics & Statistics, 6(1), 1-11.
2018 Dedduwakumara, D. S., & Prendergast, L. A. (2018). Confidence intervals for quantiles from histograms and other grouped data. Communications in Statistics: Simulation and Computation, 49(6), 1546-1559.
DOI Scopus3 WoS1
- Dedduwakumara, D. S., Prendergast, L. A., & Staudte, R. G. (n.d.). Insights and inference for the proportion below the relative poverty
Year Citation 2019 Dedduwakumara, D. S., & Prendergast, L. A. (2019). Interval Estimators for Inequality Measures Using Grouped Data. In Communications in Computer and Information Science Vol. 1150 CCIS (pp. 238-252). Singapore: Springer.
DOI Scopus1 WoS1
Statistical Practice I
Statistics and Numerical Methods II
Advanced Mathematical Perspectives II
Probability and Statistics
Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2023 Co-Supervisor Using statistical natural language processing to analyse digitised historical sources Master of Philosophy Master Full Time Mr William Pincombe
Date Role Committee Institution Country 2021 - ongoing Member Outreach Committee School of Mathematical Sciences Australia
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