Screening methods for linear errors-in-variables models in high dimensions
Date: Friday, 27 August 2021
Time: 3-4 pm
Speaker: Dr Linh Nghiem (ANU)
Abstract:
Microarray studies, in order to identify genes associated with an outcome of interest, usually produce noisy measurements for a large number of gene expression features from a small number of subjects. A common approach to analyzing such high-dimensional data is to use linear errors-in-variables models; however, current methods for fitting such models are computationally expensive due to non-convex optimization or sampling from high dimensional distributions. We develop two efficient screening procedures to reduce the number of variables for final model building; both of them have strong theoretical support and can be computed efficiently even with a large number of features. Through simulation studies and an analysis of microarray data, we demonstrate that the two new screening procedures make the estimation of linear errors-in-variables models computationally scalable in high dimensional settings, and improve finite sample estimation and selection performance compared with estimators that do not employ a screening stage.
Short Bio:
Linh Nghiem is currently a postdoctoral research fellow in Statistics at the Australian National University (ANU). His methodological research interests include measurement error modelling, graphical model estimation, sufficient dimension reduction, and exploring the human perception of music using data.