Recycled aggregates, obtained from construction and demolition waste (C&DW), are
currently underutilized in the production of new concrete given the incidence of widespread …

A model-based biclustering method for multivariate discrete longitudinal data is proposed.
We consider a finite mixture of generalized linear models to cluster units and, within each …

Urban blocks are fundamental elements of urban form and play an important role in urban
development. Identification of their typologies has been a long-standing challenge in urban …

Normal cluster-weighted models constitute a modern approach to linear regression which
simultaneously perform model-based cluster analysis and multivariate linear regression …

Investigating swimming technical skills by a double partition clustering of multivariate
functional data allowing for dimension Page 1 The Annals of Applied Statistics 2024, Vol. 18 …

The family of multiple scaled mixtures of multivariate normal (MSMN) distributions has been
shown to be a powerful tool for modeling data that allow different marginal amounts of tail …

Emotions are highly dynamic and social in nature. Traditional approaches to studying
emotion expression face obstacles such as substantial time investments, susceptibility to …

The Supplementary Material consists of five appendices. In Appendix I we derive the form of
the influence functions. In Appendix II, we show how patient-level information is not required …

Cluster analysis is a popular unsupervised learning tool used in many disciplines to identify
heterogeneous sub-populations within a sample. However, validating cluster analysis …

Bayesian clustering models, such as Dirichlet process mixture models (DPMMs), are
sophisticated flexible models. They induce a posterior distribution on the set of all partitions …