2 papers
stat.ML2022
A parallelizable model-based approach for marginal and multivariate clustering
Miguel de Carvalho, Gabriel Martos Venturini, Andrej Svetlošák
This paper develops a clustering method that takes advantage of the sturdiness of model-based clustering, while attempting to mitigate some of its pitfalls. First, we note that sta…
stat.ME2022
Uncovering Regions of Maximum Dissimilarity on Random Process Data
Miguel de Carvalho, Gabriel Martos Venturini
The comparison of local characteristics of two random processes can shed light on periods of time or space at which the processes differ the most. This paper proposes a method that…