High-Dimensional Inference

Here we propose a fully nonparametric approach to conditional density estimation that reformulates CDE as a non-parametric orthogonal series problem where the expansion coefficients are estimated by regression. By taking such an approach, one can efficiently estimate conditional densities and not just expectations in high dimensions by drawing upon the success in high-dimensional regression. We show applications to photometric galaxy data, Twitter data, and line-of-sight velocities in a galaxy cluster.

In some applications (e.g., in cosmology and economics), the regression E[Z|x] is not adequate to represent the association between a …

A key question in modern statistics is how to make fast and reliable inferences for complex, high-dimensional data. While there has …