This review examines the relevance of parameter identifiability for statistical models used in
machine learning. In addition to defining main concepts, we address several issues of …

This paper focuses on the application of recent results on lower bounds under misspecified
models to the estimation of the scatter matrix of complex elliptically symmetric (CES) …

An important prerequisite for successful multisensor integration is that the data from the
reporting sensors are transformed to a common reference frame free of systematic or …

In this paper, a complete and rigorous mathematical model for secondary surveillance radar
systematic errors (biases) is developed. The model takes into account the physical effects …

Provided that one keeps in mind the Craḿer-Rao bound (CRB) limitations, that is, to become
an overly optimistic lower bound when the observation conditions degrades, the CRB is a …

This chapter aims to provide a comprehensive overview on lower bounds on mean square
error (MSE) on the estimation of a deterministic parameter vector under misspecified …

This paper reports an extension of the existing investigations on determining identifiability of
statistical parameter models. By making use of the Kullback–Leibler divergence (KLD) in …

An important prerequisite for successful multisensor integration is that the data from the
reporting sensors are transformed to a common reference frame free of systematic or …

A lower bound on Mean Square Error (MSE) of the estimate of a real deterministic parameter
vector under misspecified model is proposed in this paper. In particular, a lower bound on …

This paper presents an identifying function (IF) approach for determining parameter structure
of statistical learning machines (SLMs). This involves studying three related aspects …