3 accepted papers
One-bit quantization has gained significant attention in communications and signal processing due to its potential for reducing energy consumption and system costs. However, parameter estimation methods that account for the quantization model tend to be complex and computationally intensive. Consequ…
In many applications of estimation theory, the true data model is not perfectly known, leading to mismatch between the assumed model used for parameter estimation and the actual model. The non-Bayesian misspecified Cramér-Rao bound (MCRB) allows considering the effect of model misspecification on th…
In many applications of estimation theory, the true data model is unknown, and a set of parameterized models are used to approximate it. This problem is encountered in learning systems, where the assumed model parameters are estimated using training data. One of the challenges in these problems is c…