Discovery Early Career Researcher Award - Grant ID: DE160101565

Funding Activity

Website
http://dataportal.arc.gov.au/NCGP/Web/Grant/Grant/DE160101565

Funding Status
Closed

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Funded Activity Summary

Flexible data modelling via skew mixture models:challenges and applications. This project seeks to explore new models for handling data with non-normal features. Parametric distributions are fundamental to statistical modelling and inference. For centuries, the ‘normal’ distribution has been the dominant model for continuous data. However, real data rarely satisfy the assumption of normality. There is thus a strong demand for more flexible distributions. This project aims to develop new methodologies in finite mixture modelling using skew component distributions to provide better models for handling data with non-normal features (such as skewness, heavy/light tails, and multimodality). Applications may include security intrusion detection, clinical diagnosis and prognosis, and flow and mass cytometry.

Funded Activity Details

Start Date: 01-01-2016

End Date: 18-01-2019

Funding Scheme: Discovery Early Career Researcher Award

Funding Amount: $330,000.00

Funder: Australian Research Council

Research Topics

ANZSRC Field of Research (FoR)

Applied Statistics | Statistical Theory | Statistics |

ANZSRC Socio-Economic Objective (SEO)

Diagnostic Methods | Expanding Knowledge in the Mathematical Sciences

Other Keywords

Applied Statistics | Diagnostic Methods | Expanding Knowledge in the Mathematical Sciences | Statistical Theory | Statistics

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