1 citations · 1 across the 3 of their papers we have counts for
4 papers
Minimum Variance Estimation of a Sparse Vector within the Linear Gaussian Model: An RKHS Approach
Alexander Jung, Sebastian Schmutzhard, Franz Hlawatsch +2
We consider minimum variance estimation within the sparse linear Gaussian model (SLGM). A sparse vector is to be estimated from a linearly transformed version embedded in Gaussian…
The RKHS Approach to Minimum Variance Estimation Revisited: Variance Bounds, Sufficient Statistics, and Exponential Families
Alexander Jung, Sebastian Schmutzhard, Franz Hlawatsch
The mathematical theory of reproducing kernel Hilbert spaces (RKHS) provides powerful tools for minimum variance estimation (MVE) problems. Here, we extend the classical RKHS based…
Performance Bounds for Sparse Parametric Covariance Estimation in Gaussian Models
Alexander Jung, Sebastian Schmutzhard, Franz Hlawatsch +1
We consider estimation of a sparse parameter vector that determines the covariance matrix of a Gaussian random vector via a sparse expansion into known "basis matrices". Using the…
A Lower Bound on the Estimator Variance for the Sparse Linear Model
Sebastian Schmutzhard, Alexander Jung, Franz Hlawatsch +2
We study the performance of estimators of a sparse nonrandom vector based on an observation which is linearly transformed and corrupted by additive white Gaussian noise. Using the…