Distributionally robust risk evaluation with an isotonic constraint
Statistical learning under distribution shift is challenging when neither prior knowledge nor
fully accessible data from the target distribution is available. Distributionally robust learning …
fully accessible data from the target distribution is available. Distributionally robust learning …
Non-Bayesian post-model-selection estimation as estimation under model misspecification
N Harel, T Routtenberg - IEEE Transactions on Signal …, 2024 - ieeexplore.ieee.org
In many parameter estimation problems, the exact model is unknown. In such cases, a
predetermined data-based selection rule selects a parametric model from a set of …
predetermined data-based selection rule selects a parametric model from a set of …
Towards robust data-driven underwater acoustic localization: A deep cnn solution with performance guarantees for model mismatch
Key challenges in developing underwater acoustic localization methods are related to the
combined effects of high reverberation in intricate environments. To address such …
combined effects of high reverberation in intricate environments. To address such …
Cyclic Misspecified Cramer-Rao Bound for Periodic Parameter Estimation
In many practical parameter estimation problems, the observation model is periodic with
respect to the unknown parameters. In these cases, the appropriate estimation criterion is …
respect to the unknown parameters. In these cases, the appropriate estimation criterion is …
A gradient-based optimization approach for underwater acoustic source localization
Conventional model-based underwater acoustic (UWA) localization algorithms such as
matched field processing (MFP) 1 are highly dependent upon accurate environmental …
matched field processing (MFP) 1 are highly dependent upon accurate environmental …