21 citations · 73 across the 29 of their papers we have counts for
30 papers
On tail-robust autocovariance matrix estimation for high-dimensional and potentially nonstationary time series
Haotian Xu, Stephane Guerrier, Runze Li +1
In this paper, we study the autocovariance matrix estimation and inference problems under heavy-tailedness, high-dimensionality, general nonlinear temporal dependence, and potentia…
Towards Open Science: Monitoring Crustal Deformations in North America
Lionel Voirol, Haotian Xu, Yuming Zhang +3
The study of the Earth's behavior has greatly benefited from the widespread deployment of Global Navigation Satellite Systems (GNSS), enabling large-scale monitoring of crustal def…
Accurate Inference for Penalized Logistic Regression
Yuming Zhang, Stéphane Guerrier, Runze Li
Inference for high-dimensional logistic regression models using penalized methods has been a challenging research problem. As an illustration, a major difficulty is the significant…
Inference for Large Scale Regression Models with Dependent Errors
Lionel Voirol, Haotian Xu, Yuming Zhang +3
The exponential growth in data sizes and storage costs has brought considerable challenges to the data science community, requiring solutions to run learning methods on such data.…
An accurate percentile method for parametric inference based on asymptotically biased estimators
Samuel Orso, Mucyo Karemera, Maria-Pia Victoria-Feser +1
Inference methods for computing confidence intervals in parametric settings usually rely on consistent estimators of the parameter of interest. However, it may be computationally a…
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…
A penalized two-pass regression to predict stock returns with time-varying risk premia
Gaetan Bakalli, Stéphane Guerrier, Olivier Scaillet
We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintai…
The Generalized Method of Wavelet Moments with Exogenous Inputs: a Fast Approach for the Analysis of GNSS Position Time Series
Davide A. Cucci, Lionel Voirol, Gaël Kermarrec +2
The Global Navigation Satellite System (GNSS) daily position time series are often described as the sum of stochastic processes and geophysical signals which allow studying global…
Just Identified Indirect Inference Estimator: Accurate Inference through Bias Correction
Yuming Zhang, Yanyuan Ma, Samuel Orso +3
An important challenge in statistical analysis lies in controlling the estimation bias when handling the ever-increasing data size and model complexity of modern data settings. In…
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…
Prevalence Estimation from Random Samples and Census Data with Participation Bias
Stéphane Guerrier, Christoph Kuzmics, Maria-Pia Victoria-Feser
Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population with unknown participation bias. For prevalence estimation, the offici…
A General Approach for Simulation-based Bias Correction in High Dimensional Settings
Stéphane Guerrier, Mucyo Karemera, Samuel Orso +2
An important challenge in statistical analysis lies in controlling the bias of estimators due to the ever-increasing data size and model complexity. Approximate numerical methods a…
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…
Asymptotically Optimal Bias Reduction for Parametric Models
Stéphane Guerrier, Mucyo Karemera, Samuel Orso +1
An important challenge in statistical analysis concerns the control of the finite sample bias of estimators. This problem is magnified in high-dimensional settings where the number…
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…
Phase Transition Unbiased Estimation in High Dimensional Settings
Stéphane Guerrier, Mucyo Karemera, Samuel Orso +1
An important challenge in statistical analysis concerns the control of the finite sample bias of estimators. For example, the maximum likelihood estimator has a bias that can resul…
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 simple recipe for making accurate parametric inference in finite sample
Stéphane Guerrier, Mucyo Karemera, Samuel Orso +1
Constructing tests or confidence regions that control over the error rates in the long-run is probably one of the most important problem in statistics. Yet, the theoretical justifi…
On the Properties of Simulation-based Estimators in High Dimensions
Stéphane Guerrier, Mucyo Karemera, Samuel Orso +1
Considering the increasing size of available data, the need for statistical methods that control the finite sample bias is growing. This is mainly due to the frequent settings wher…
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…