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stat.ME2026
Testing Covariance Separability in High Dimensions
Tomas Masak, Marcus Mayrhofer, Una Radojičić
Separability is an important structural assumption often placed on the covariance when working with matrix-variate data, because it greatly simplifies both interpretation and compu…
stat.ME2023
The Functional Graphical Lasso
Kartik G. Waghmare, Tomas Masak, Victor M. Panaretos
We consider the problem of recovering conditional independence relationships between jointly distributed Hilbertian random elements given realizations thereof. We operate i…
stat.ME2021
Inference and Computation for Sparsely Sampled Random Surfaces
Tomas Masak, Tomas Rubin, Victor Panaretos
Non-parametric inference for functional data over two-dimensional domains entails additional computational and statistical challenges, compared to the one-dimensional case. Separab…