Bootstrapping Latent Class Models. - José G. Dias (GfKl, 2004)

An empirical comparison of EM, SEM and MCMC performance for problematic Gaussian mixture likelihoods. - José G. Dias; Michel Wedel (Stat. Comput., 2004)

Model Selection Criteria for Model-Based Clustering of Categorical Time Series Data: A Monte Carlo Study. - José G. Dias (GfKl, 2006)

Mixture Hidden Markov Models in Finance Research. - José G. Dias; Jeroen K. Vermunt; Sofia Ramos (GfKl, 2008)

The SKM Algorithm: A K-Means Algorithm for Clustering Sequential Data. - José G. Dias; Maria João Cortinhal (IBERAMIA, 2008)

A bootstrap-based aggregate classifier for model-based clustering. - José G. Dias; Jeroen K. Vermunt (Comput. Stat., 2008)

Latent Class Models of Time Series Data: An Entropic-Based Uncertainty Measure. - José G. Dias (Algorithms from and for Nature and Life, 2013)

Model-Based Clustering of Multistate Data with Latent Change: An Application with DHS Data. - José G. Dias (Classification and Data Mining, 2013)

Mining categorical sequences from data using a hybrid clustering method. - Luca De Angelis; José G. Dias (Eur. J. Oper. Res., 2014)

Dynamic clustering of energy markets: An extended hidden Markov approach. - José G. Dias; Sofia Ramos (Expert Syst. Appl., 2014)

Clustering financial time series: New insights from an extended hidden Markov model. - José G. Dias; Jeroen K. Vermunt; Sofia Ramos (Eur. J. Oper. Res., 2015)

Survival mixture models in behavioral scoring. - Bruno Cardoso Alves; José G. Dias (Expert Syst. Appl., 2015)

Probabilistic clustering of interval data. - Paula Brito; António Pedro Duarte Silva; José G. Dias (Intell. Data Anal., 2015)

Extracting clusters from aggregate panel data: A market segmentation study. - Graça Trindade; José G. Dias; Jorge A. Ambrósio (Appl. Math. Comput., 2017)

MAINT.Data: Modelling and Analysing Interval Data in R. - António Pedro Duarte Silva; Paula Brito; Peter Filzmoser; José G. Dias (R J., 2021)