Optimal joint detection and estimation under distributional uncertainty is achieved by maximizing an f-similarity to identify least favorable distributions in both Bayesian and Neyman-Pearson settings.
An approach to joint sequential detection and estimation with distri- butional uncertainties,
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Minimax Optimal Procedures for Joint Detection and Estimation
Optimal joint detection and estimation under distributional uncertainty is achieved by maximizing an f-similarity to identify least favorable distributions in both Bayesian and Neyman-Pearson settings.