  Statistical analysis of massive datasets very often implies expensive linear
algebra operations with large dense matrices. Typical tasks are an estimation
of unknown parameters of the underlying statistical model and prediction of
missing values. We developed the H-MLE procedure, which solves these tasks. The
unknown parameters can be estimated by maximizing the joint Gaussian
log-likelihood function, which depends on a covariance matrix. To decrease high
computational cost, we approximate the covariance matrix in the hierarchical
(H-) matrix format. The H-matrix technique allows us to work with inhomogeneous
covariance matrices and almost arbitrary locations. Especially, H-matrices can
be applied in cases when the matrices under consideration are dense and
unstructured.
  For validation purposes, we implemented three machine learning methods: the
k-nearest neighbors (kNN), random forest, and deep neural network. The best
results (for the given datasets) were obtained by the kNN method with three or
seven neighbors depending on the dataset. The results computed with the H-MLE
method were compared with the results obtained by the kNN method.
  The developed H-matrix code and all datasets are freely available online.
