Publications (10)
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…
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…
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…
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…
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…
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…
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…
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…
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…
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…