depmix

depmix: Dependent Mixture Models. Fit (multigroup) mixtures of latent Markov models on mixed categorical and continuous (timeseries) data

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References in zbMATH (referenced in 7 articles )

Showing results 1 to 7 of 7.
Sorted by year (citations)

  1. Chow, Sy-Miin; Ou, Lu; Ciptadi, Arridhana; Prince, Emily B.; You, Dongjun; Hunter, Michael D.; Rehg, James M.; Rozga, Agata; Messinger, Daniel S.: Representing sudden shifts in intensive dyadic interaction data using differential equation models with regime switching (2018)
  2. Zuanetti, Daiane Aparecida; Milan, Luis Aparecido: A generalized mixture model applied to diabetes incidence data (2017)
  3. Kazor, Karen; Hering, Amanda S.: Assessing the performance of model-based clustering methods in multivariate time series with application to identifying regional wind regimes (2015)
  4. Visser, Ingmar; Speekenbrink, Maarten: Comments on: “Latent Markov models: a review of a general framework for the analysis of longitudinal data with covariates” (2014)
  5. Chow, Sy-Miin; Zhang, Guangjian: Nonlinear regime-switching state-space (RSSS) models (2013)
  6. Colombi, Roberto; Giordano, Sabrina: Testing lumpability for marginal discrete hidden Markov models (2011)
  7. Visser, Ingmar: Seven things to remember about hidden Markov models: A tutorial on Markovian models for time series (2011)