3 papers
stat.ME2024
The quantile-based classifier with variable-wise parameters
Marco Berrettini, Christian Hennig, Cinzia Viroli
Quantile-based classifiers can classify high-dimensional observations by minimising a discrepancy of an observation to a class based on suitable quantiles of the within-class distr…
stat.ME2023
Choice of trimming proportion and number of clusters in robust clustering based on trimming
Luis Angel García-Escudero, Christian Hennig, Agustín Mayo-Iscar +2
So-called "classification trimmed likelihood curves" have been proposed as a useful heuristic tool to determine the number of clusters and trimming proportion in trimming-based rob…
stat.ML2023
Some issues in robust clustering
Christian Hennig
Some key issues in robust clustering are discussed with focus on Gaussian mixture model based clustering, namely the formal definition of outliers, ambiguity between groups of outl…