Bootstrap methods for hypothesis testing in non-linear models 

Project description

Hypothesis testing is an important way to draw scientific conclusions from experimental data. However, models relevant in industrial settings (for example models that characterise manufacturing processes) are almost invariably non-linear in the model parameters, and hypotheses of interest often involve parameters that lie on the boundary of the parameter space; these are challenging to standard (asymptotic) approaches to hypothesis testing. We will develop methods based on the "bootstrap" -- a powerful approach in computational statistics that involves computing null distributions using simulated data -- to address hypothesis testing in challenging non-linear settings.