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 signal processing, with a focus reflected in its title: Accounting for Vibration Noise in Stochastic Measurement Errors of Inertial Sensors.
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 signal processing, with a focus reflected in its title: Accounting for Vibration Noise in Stochastic Measurement Errors.
This publication contributes to work in robust statistics, signal processing, with a focus reflected in its title: Robust and Scalable Inference for Stochastic Processes.
Sensor calibration is usually performed only on one error signal although calibration sessions often collect repeated samples. This work formally puts forward and studies adequate solutions to consider all replicates to improve predictive accuracy.
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.
This work puts forward an approach to test causal links between irregularly sampled signals with applications to nitrogen signalling between roots and shoots of plants
This work compares current moment-matching techniques used to calibrate inertial sensors and identifies the optimal technique from a theoretical (and applied) perspective