76 citations · 100 across the 8 of their papers we have counts for
8 papers
Statistical Theory in the Age of Machine-Assisted Mathematics: Rethinking How Theory Is Made and Taught
Pietro Coretto
The computational revolution is advancing at an unprecedented pace. The combination of proof-assistant technologies and generative AI tools has recently enabled the solution of com…
Consistency for constrained maximum likelihood estimation and clustering based on mixtures of elliptically-symmetric distributions under general data generating processes
Pietro Coretto, Christian Hennig
The consistency of the maximum likelihood estimator for mixtures of elliptically-symmetric distributions for estimating its population version is shown, where the underlying distri…
Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score
Luca Coraggio, Pietro Coretto
Cluster analysis requires many decisions: the clustering method and the implied reference model, the number of clusters and, often, several hyper-parameters and algorithms' tunings…
An adequacy approach for deciding the number of clusters for OTRIMLE robust Gaussian mixture based clustering
Christian Hennig, Pietro Coretto
We introduce a new approach to deciding the number of clusters. The approach is applied to Optimally Tuned Robust Improper Maximum Likelihood Estimation (OTRIMLE; Coretto and Henni…
A fast subsampling method for estimating the distribution of signal-to-noise ratio statistics in nonparametric time series regression models
Francesco Giordano, Pietro Coretto
Signal-to-noise ratio (SNR) statistics play a central role in many applications. A common situation where SNR is studied is when a continuous time signal is sampled at a fixed freq…
Robust improper maximum likelihood: tuning, computation, and a comparison with other methods for robust Gaussian clustering
Pietro Coretto, Christian Hennig
The two main topics of this paper are the introduction of the "optimally tuned improper maximum likelihood estimator" (OTRIMLE) for robust clustering based on the multivariate Gaus…
Nonparametric estimation of the dynamic range of music signals
Pietro Coretto, Francesco Giordano
The dynamic range is an important parameter which measures the spread of sound power, and for music signals it is a measure of recording quality. There are various descriptive meas…
Consistency, breakdown robustness, and algorithms for robust improper maximum likelihood clustering
Pietro Coretto, Christian Hennig
The robust improper maximum likelihood estimator (RIMLE) is a new method for robust multivariate clustering finding approximately Gaussian clusters. It maximizes a pseudo-likelihoo…