38 citations · 66 across the 14 of their papers we have counts for
14 papers
Multivariate Adjustments for Average Equivalence Testing
Younes Boulaguiem, Luca Insolia, Maria-Pia Victoria-Feser +2
Multivariate (average) equivalence testing is widely used to assess whether the means of two conditions of interest are `equivalent' for different outcomes simultaneously. The mult…
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…
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…
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…
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…
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 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…
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 Prediction Divergence Criterion for Model Selection
Stephane Guerrier, Maria-Pia Victoria-Feser
The problem of model selection is inevitable in an increasingly large number of applications involving partial theoretical knowledge and vast amounts of information, like in medici…
Robust VIF regression with application to variable selection in large data sets
Debbie J. Dupuis, Maria-Pia Victoria-Feser
The sophisticated and automated means of data collection used by an increasing number of institutions and companies leads to extremely large data sets. Subset selection in regressi…
Zero-inflated truncated generalized Pareto distribution for the analysis of radio audience data
Dominique-Laurent Couturier, Maria-Pia Victoria-Feser
Extreme value data with a high clump-at-zero occur in many domains. Moreover, it might happen that the observed data are either truncated below a given threshold and/or might not b…