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Bayesian Post-Model-Selection Estimation
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DOI：10.1109/LSP.2020.3048830.png)
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摘要
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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 Cramer-Rao bound (BCRB) and selective tighter BCRB, which are lower bounds on the mean-squared-error (MSE) for any coherent estimator.
Keyword:
Bayesian parameter estimation
coherence estimation
estimation after model selection
selective Bayesian Cramer-Rao bound
sparse recovery
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9.6
论文数:
1.1W
被引数:
1.7W
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