76 citations · 100 across the 8 of their papers we have counts for
4 papers · 1 filter
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…
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…