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. While various solutions have been suggested, the majority of them have been designed to account for only one of these challenges. In this work, we propose a flexible and efficient latent-variable model that is capable of addressing all …

challenges cs.lg data datasets fields healthcare latent variable model measurement patterns process seismology sociology solutions stat.ml stochastic stochastic process world