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Found 3 papers, 2 papers with code
Model-based Clustering with Missing Not At Random Data
In this paper, we propose model-based clustering algorithms designed to handle very general types of missing data, including MNAR data.
Mixture of hidden Markov models for accelerometer data
Motivated by the analysis of accelerometer data, we introduce a specific finite mixture of hidden Markov models with particular characteristics that adapt well to the specific nature of this type of data.
Methodology Statistics Theory Statistics Theory
A Family of Blockwise One-Factor Distributions for Modelling High-Dimensional Binary Data
We introduce a new family of one factor distributions for high-dimensional binary data.
Methodology
