[Cite as http://purl.org/au-research/grants/arc/DP170100987]
Researchers A/Prof Youngki Shin; Prof Sokbae Lee; Asst Prof Yuan Liao; Dr Myung Seo
Brief description This project aims to provide a set of estimation and inference procedures for high dimensional quantile regression. Statistical models of threshold regression with change or tipping points are used to explore social issues, including changes in oil and gas prices, effective dosage of drugs and the racial mix in neighbourhoods. To date, using low numbers of variables, the findings have been limited. Big data makes it possible and desirable to solve more detailed models to provide more accurate results. The quality and accuracy of the project’s results are expected to help governments devise well informed and appropriate policies for social issues.
Funding Amount $200,000
Funding Scheme Discovery Projects