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 ...

Locally Robust Semiparametrically Efficient Bayesian Inference. (Joint with ANDRIY NORETS.) Slides. We propose a framework for making Bayesian parametric models robust to local misspecification.

Irregular functional data, in which densely sampled curves are observed over different ranges, pose a challenge for modeling and inference, and sensitivity to outlier curves is a concern in ...