Name
Abstract | ||
|---|---|---|
Practical multi-parameter estimation problems with limited measurements and unidentifiable scenarios usually involve a preliminary data-based selection stage. Recent works have shown that selection-aware estimation methods outperform state-of-the-art estimators in the sense of post-selection bias and post-selection mean-squared-error (PSMSE). In this paper, we discuss non-Bayesian estimation methods where a subset of parameters is selected for estimation from the full unknown parameter vector by a data-based selection-rule. We present four estimators: the maximum likelihood (ML), the coherent ML, the post-selection ML (PSML), and the coherent PSML. Coherent post-selection estimators force the unselected parameters to zero, and thus, can be implemented in practical high-dimensional problems. Additionally, we develop a low-complexity algorithm, the stochastic approximation PSML (SA-PSML) for practical implementation of the coherent PSML estimator. Simulation results show that the SA-PSML algorithm achieves a lower PSMSE than the coherent ML estimator for sparse vector recovery with the orthogonal matching pursuit (OMP) selection rule. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/SSP49050.2021.9513852 | 2021 IEEE Statistical Signal Processing Workshop (SSP) |
Keywords | DocType | ISSN |
Estimation after parameter selection,post-selection maximum-likelihood,orthogonal matching pursuit,stochastic approximation,unidentifiable models | Conference | 2373-0803 |
ISBN | Citations | PageRank |
978-1-7281-5768-9 | 0 | 0.34 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nadav Harel | 1 | 0 | 0.34 |
| Tirza Routtenberg | 2 | 0 | 0.34 |