Robust Statistics

This publication contributes to work in data privacy, robust statistics, with a focus reflected in its title: Equivalence Testing Under Privacy Constraints.

This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: Inference for Large Scale Regression Models With Dependent Errors.

This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: Wavelet Variance Based Robust Estimation of Composite Stochastic Models.

This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: Robust and Scalable Inference for Stochastic Processes.

Using a wavelet-decompositon of a random field, the aim is to efficiently estimate (latent) spatial models.

There is considerable research on improving navigation accuracy of unmanned (aerial) vehicles. This direction of research focuses on improving stochastic sensor calibration to integrate navigation filters and produce more accurate navigation performance.

In this work we put forward a computationally efficient statistical procedure to estimate a wide range of time series models in a robust manner thereby reducing the influence of data contamination on estimation and inference.

This paper introduces a robust, user-friendly R-based software platform using the generalized method of wavelet moments for efficient and statistically sound calibration of stochastic errors in inertial sensors, even with outlier-affected data

This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: An R Package for Robust Time Series Analysis.

This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: Fast and Robust Parametric Estimation for Time Series and Spatial Models.