2 papers
stat.ME2026
Flexible modeling of bimodal distributions via skewed- mixtures
Marco Bee, Flavio Santi
We propose a mixture of location-scale skewed- distributions to fit bimodal, skewed and heavy-tailed data. In particular, the mixture is based on the skewed- distribution by…
stat.ME2022
Unsupervised Mixture Estimation via Approximate Maximum Likelihood based on the Cramér - von Mises distance
Marco Bee
Mixture distributions with dynamic weights are an efficient way of modeling loss data characterized by heavy tails. However, maximum likelihood estimation of this family of models…