Robust inference in time series analysis is concerned with developing statistical methods that remain valid under departures from standard model assumptions, such as the presence of heteroskedasticity ...

This paper investigates new aspects of robust inference for general linear models, calling for a broader array of error measures, beyond the conventional notion of ...

Future applications of national importance, such as healthcare, critical infrastructure, transportation systems, and smart cities, are expected to increasingly rely on machine-learning methods, ...

Instrumental variable (IV) methods for regression are well established. More recently, methods have been developed for statistical inference when the instruments are weakly correlated with the ...