Likelihood Estimation with Incomplete Array Variate Observations
This work addresses missing data challenges for researchers handling multiway data, but it appears incremental as it builds on existing array variate models.
The paper tackles the problem of missing data in high-dimensional multiway arrays by proposing methods for parameter estimation of array variate normal models, with applications in imputation and covariance estimation, demonstrated through simulations and genetic data.
Missing data is an important challenge when dealing with high dimensional data arranged in the form of an array. In this paper, we propose methods for estimation of the parameters of array variate normal probability model from partially observed multiway data. The methods developed here are useful for missing data imputation, estimation of mean and covariance parameters for multiway data. A multiway semi-parametric mixed effects model that allows separation of multiway covariance effects is also defined and an efficient algorithm for estimation is recommended. We provide simulation results along with real life data from genetics to demonstrate these methods.