B1783
Title: Supersaturated design-based statistical methods for variable selection
Authors: Tharkeshi Dharmaratne - RMIT University (Australia) [presenting]
Alysha De Livera - La Trobe University (Australia)
Stelios Georgiou - RMIT University (Australia)
Stella Stylianou - RMIT University (Australia)
Abstract: In experimental studies, supersaturated screening design (SSD)-based statistical methods are commonly used to screen relevant factors when the number of factors exceeds the run size. Based on simulation studies, several of these SSD methods have shown to be performing well in experimental settings. It motivated The exploration of the use of these SSD methods on observational data for variable selection. Variable selection is a widely-used approach for selecting variables of a statistical model in observational studies, which has often been criticised. Therefore, initially reviewed the latest recommendations and methods that are developed for variable selection in observational studies. The performance of the SSD-based statistical methods is then evaluated using both simulated and real-life datasets, followed by a comparison of their performance with the existing approaches.
