7 papers
Tight Majorizations and Convergence Rates of Nuclear Norm Minimization IRLS
Christian Kümmerle, Tomas Masak, Dominik Stöger
Iteratively reweighted least squares (IRLS) methods constitute a natural approach to nuclear norm minimization, but their convergence rates and the role of the weight operator have…
Amplitude-Phase Analysis of the COVID-19 Point Process and the Early Countermeasures
Francesco Tripoli, Leonardo V. Santoro, Tomas Masak
We investigate how governmental restrictions relate to the spread and temporal dynamics of COVID-19 early in the pandemic. We model daily infection data from each US state as reali…
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…
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…
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…
Separable Expansions for Covariance Estimation
Tomas Masak, Soham Sarkar, Victor M. Panaretos
The non-parametric estimation of covariance lies at the heart of functional data analysis, whether for curve or surface-valued data. The case of a two-dimensional domain poses both…
Random Surface Covariance Estimation by Shifted Partial Tracing
Tomas Masak, Victor M. Panaretos
The problem of covariance estimation for replicated surface-valued processes is examined from the functional data analysis perspective. Considerations of statistical and computatio…