11 citations · 48 across the 15 of their papers we have counts for
16 papers
Accounting for Vibration Noise in Stochastic Measurement Errors
Lionel Voirol, Davide A. Cucci, Mucyo Karemera +3
The measurement of data over time and/or space is of utmost importance in a wide range of domains from engineering to physics. Devices that perform these measurements therefore nee…
Perturbed M-Estimation: A Further Investigation of Robust Statistics for Differential Privacy
Aleksandra Slavkovic, Roberto Molinari
Differential Privacy (DP) provides an elegant mathematical framework for defining a provable disclosure risk in the presence of arbitrary adversaries; it guarantees that whether an…
Scale-wise Variance Minimization for Optimal Virtual Signals: An Approach for Redundant Gyroscopes
Yuming Zhang, Davide A. Cucci, Roberto Molinari +1
The increased use of low-cost gyroscopes within inertial sensors for navigation purposes, among others, has brought to the development of a considerable amount of research in impro…
Multi-Signal Approaches for Repeated Sampling Schemes in Inertial Sensor Calibration
Gaetan Bakalli, Davide A. Cucci, Ahmed Radi +4
Inertial sensor calibration plays a progressively important role in many areas of research among which navigation engineering. By performing this task accurately, it is possible to…
SWAG: A Wrapper Method for Sparse Learning
Roberto Molinari, Gaetan Bakalli, Stéphane Guerrier +4
The majority of machine learning methods and algorithms give high priority to prediction performance which may not always correspond to the priority of the users. In many cases, pr…
Robust Two-Step Wavelet-Based Inference for Time Series Models
Stéphane Guerrier, Roberto Molinari, Maria-Pia Victoria-Feser +1
Complex time series models such as (the sum of) ARMA models with additional noise, random walks, rounding errors and/or drifts are increasingly used for data analysis in fie…
Wavelet-Based Moment-Matching Techniques for Inertial Sensor Calibration
Stéphane Guerrier, Juan Jurado, Mehran Khaghani +9
The task of inertial sensor calibration has required the development of various techniques to take into account the sources of measurement error coming from such devices. The calib…
Multivariate Signal Modelling with Applications to Inertial Sensor Calibration
Haotian Xu, Stéphane Guerrier, Roberto Molinari +1
The common approach to inertial sensor calibration for navigation purposes has been to model the stochastic error signals of individual sensors independently, whether as components…
A Study of the Allan Variance for Constant-Mean Non-Stationary Processes
Haotian Xu, Stéphane Guerrier, Roberto Molinari +1
The Allan Variance (AV) is a widely used quantity in areas focusing on error measurement as well as in the general analysis of variance for autocorrelated processes in domains such…
On the Identifiability of Latent Models for Dependent Data
Stéphane Guerrier, Roberto Molinari
The condition of parameter identifiability is essential for the consistency of all estimators and is often challenging to prove. As a consequence, this condition is often assumed f…
Fast and Robust Parametric Estimation for Time Series and Spatial Models
Stéphane Guerrier, Roberto Molinari
We present a new framework for robust estimation and inference on second-order stationary time series and random fields. This framework is based on the Generalized Method of Wavele…
Wavelet Variance for Random Fields: an M-Estimation Framework
Stéphane Guerrier, Roberto Molinari
We present a general M-estimation framework for inference on the wavelet variance. This framework generalizes the results on the scale-wise properties of the standard estimator and…
The gmwm R package: a comprehensive tool for time series analysis from state-space models to robustness
James Balamuta, Roberto Molinari, Stéphane Guerrier +1
The gmwm R package for inference on time series models is mainly based on the quantity called wavelet variance which is derived from a wavelet decomposition of a time series. This…
A Computationally Efficient Framework for Automatic Inertial Sensor Calibration
James Balamuta, Stephane Guerrier, Roberto Molinari +1
The calibration of (low-cost) inertial sensors has become increasingly important over the past years since their use has grown exponentially in many applications going from unmanne…
Robust Inference for Time Series Models: a Wavelet-based Framework
Stephane Guerrier, Roberto Molinari
We present a new framework for the robust estimation of latent time series models which is fairly general and, for example, covers models going from ARMA to state-space models. Thi…
A Paradigmatic Regression Algorithm for Gene Selection Problems
Stéphane Guerrier, Nabil Mili, Roberto Molinari +3
Motivation: Gene selection has become a common task in most gene expression studies. The objective of such research is often to identify the smallest possible set of genes that can…