15 citations · 19 across the 9 of their papers we have counts for
9 papers
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…
Equivalence Testing Under Privacy Constraints
Savita Pareek, Luca Insolia, Roberto Molinari +1
Protecting individual privacy is essential across research domains, from socio-economic surveys to big-tech user data. This need is particularly acute in healthcare, where analyses…
Bridging the gap between experimental burden and statistical power for quantiles equivalence testing
Jun Wu, Stéphane Guerrier, Si Gou +2
Testing the equivalence of multiple quantiles between two populations is important in many scientific applications, such as clinical trials, where conventional mean-based methods m…
Bioequivalence Assessment for Locally Acting Drugs: A Framework for Feasible and Efficient Evaluation
Luca Insolia, Yanyuan Ma, Younes Boulaguiem +1
Equivalence testing plays a key role in several domains, such as the development of generic medical products, which are therapeutically equivalent to brand-name drugs but with redu…
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…
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.…
Tk-merge: Computationally Efficient Robust Clustering Under General Assumptions
Luca Insolia, Domenico Perrotta
We address general-shaped clustering problems under very weak parametric assumptions with a two-step hybrid robust clustering algorithm based on trimmed k-means and hierarchical ag…
Doubly Robust Feature Selection with Mean and Variance Outlier Detection and Oracle Properties
Luca Insolia, Francesca Chiaromonte, Runze Li +1
We propose a general approach to handle data contaminations that might disrupt the performance of feature selection and estimation procedures for high-dimensional linear models. Sp…
Simultaneous Feature Selection and Outlier Detection with Optimality Guarantees
Luca Insolia, Ana Kenney, Francesca Chiaromonte +1
Sparse estimation methods capable of tolerating outliers have been broadly investigated in the last decade. We contribute to this research considering high-dimensional regression p…