Latent variable model for high-dimensional point process with structured missingness

doi:doi:10.57702/zevcs60t

Latent variable model for high-dimensional point process with structured missingness

Longitudinal data are important in numerous fields, such as healthcare, sociology and seismology, but real-world datasets present notable challenges for practitioners because they can be high-dimensional, contain structured missingness patterns, and measurement time points can be governed by an unknown stochastic process.

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The json representation of the dataset with its distributions based on DCAT.
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Cite this as

Maksim Sinelnikov, Manuel Haussmann, Harri L¨ahdesm¨aki (2024). Dataset: Latent variable model for high-dimensional point process with structured missingness. https://doi.org/10.57702/zevcs60t

DOI retrieved: December 16, 2024

Additional Info

Field	Value
Created	December 16, 2024
Last update	December 16, 2024
Author	Maksim Sinelnikov
More Authors	Manuel Haussmann Harri L¨ahdesm¨aki