38 citations · 66 across the 14 of their papers we have counts for
8 papers · 1 filter
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…
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…
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…
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…