The task to write on data analysis on nonstandard spaces is quite substantial, with a huge
body of literature to cover, from parametric to nonparametrics, from shape spaces to …

Two central limit theorems for sample Fréchet means are derived, both significant for
nonparametric inference on non-Euclidean spaces. The first theorem encompasses and …

The (CLT) central limit theorems for generalized Fréchet means (data descriptors assuming
values in manifolds, such as intrinsic means, geodesics, etc.) on manifolds from the literature …

The location estimation problem has been attracting a lot of research interest in recent years
due to its significance for different areas of signal processing. This paper deals with a bistatic …

This paper gives a comprehensive treatment of local uniqueness, asymptotics and numerics
for intrinsic sample means on the circle. It turns out that local uniqueness as well as rates of …

The Cramér-Rao bound (CRB) is one of the most important tools for performance analysis in
parameter estimation. In many practical periodic parameter estimation problems, the …

Estimating the coefficients of a noisy polynomial phase signal is important in fields including
radar, biology and radio communications. One approach attempts to perform polynomial …

The Cramér-Rao bound (CRB) has been extensively used as a benchmark for estimation
performance in both Bayesian and non-Bayesian frameworks. In many practical periodic …

In many practical periodic parameter estimation problems, the appropriate performance
criteria are periodic in the parameter space. The existing mean-square-error (MSE) lower …

In this paper, we improve the minimum squared error (MSE) algorithm for classification by
modifying its classification rule. Differing from the conventional MSE algorithm which first …