A structural model on a hypercube represented by optimal transport

We propose a flexible statistical model for high-dimensional quantitative data on a hypercube. Our model, called the structural gradient model (SGM), is based on a one-to-one map on the hypercube that is a solution for an optimal transport problem. As we show with many examples, SGM can describe various dependence structures including correlation and heteroscedasticity. The maximum likelihood estimation of SGM is effectively solved by the determinant-maximization programming. In particular, a lasso-type estimation is available by adding constraints. SGM is compared with graphical Gaussian models and mixture models.

💡 Research Summary

The paper introduces the Structural Gradient Model (SGM), a novel statistical framework for handling high‑dimensional quantitative data that reside on a hypercube (