Abstract
Estimation after model selection refers to the problem where the exact observation model is unknown and is assumed to belong to a set of candidate models. Thus, a data-based model-selection stage is performed prior to the parameter estimation stage, which affects the performance of the subsequent estimation. In this letter, we investigate post-model-selection Bayesian parameter estimation of a random vector with an unknown deterministic support set, where this support set represents the model. First, we present different estimators, including the oracle minimum mean-squared-error (MMSE), the coherent MMSE, the selected MMSE, and the full model MMSE. Then, we develop the selective Bayesian Cram$\acute{\text{e}}$r-Rao bound (BCRB) and selective tighter BCRB, which are lower bounds on the mean-squared-error (MSE) for any coherent estimator.
| Original language | English |
|---|---|
| Article number | 9314021 |
| Pages (from-to) | 175-179 |
| Number of pages | 5 |
| Journal | IEEE Signal Processing Letters |
| Volume | 28 |
| DOIs | |
| State | Published - 1 Jan 2021 |
Keywords
- Bayesian parameter estimation
- coherence estimation
- estimation after model selection
- selective Bayesian Cramer-Rao bound
- sparse recovery
ASJC Scopus subject areas
- Signal Processing
- Electrical and Electronic Engineering
- Applied Mathematics