Since the track-length estimator is the best solo estimator, the comparison in the above Vgure of the combined estimator to the track-length estimator is a realistic evaluation of the combined estimator. Values larger than 1.0 indicate that the combined estimator performs better than the track-length estimator. The dark central line is the mean fom ratio over the slab width, while the shaded ba...

In this paper, we address the distributed filtering and prediction of time-varying random fields represented by linear time-invariant (LTI) dynamical systems. The field is observed by a sparsely connected network of agents/sensors collaborating among themselves. We develop a Kalman filter type consensus+innovations distributed linear estimator of the dynamic field termed as Consensus+Innovation...

This paper deals with the optimal design of quadratic NonData-Aided (NDA) openand closed-loop estimators. The new approach supplies the minimumvariance, unbiasedNDA quadratic estimators, without the need of assuming a given statistics for the nuisance parameters, that is, avoiding the common adoption of the gaussian assumption, which does not apply in digital communications. Alternatively, if t...

We give an explicit error bound between the invariant density of an elliptic reflected diffusion in a smooth compact domain and the kernel estimator built on the symmetric Euler scheme introduced in Bossy, Gobet and Talay (2004).

We show how to construct the best linear unbiased predictor (BLUP) for the continuation of a curve in a spline-function model. We assume that the entire curve is drawn from some smooth random process and that the curve is given up to some cut point. We demonstrate how to compute the BLUP efficiently. Confidence bands for the BLUP are discussed. Finally, we apply the proposed BLUP to real-world ...

In this paper we examine the accuracy and precision of three indices of catch-per-unit-effort (CPUE). We carried out simulations, generating catch data according to six probability distributions (normal, Poisson, lognormal, gamma, delta and negative binomial), three variance structures (constant, proportional to effort and proportional to the squared effort) and their magnitudes (tail weight). ...

• A simple generalisation of the classical Hill estimator of a positive extreme value index (EVI) has been recently introduced in the literature. Indeed, the Hill estimator can be regarded as the logarithm of the mean of order p = 0 of a certain set of statistics. Instead of such a geometric mean, we can more generally consider the mean of order p (MOP) of those statistics, with p real, and eve...

Following the Gauss-Markov theorem the generalized lest-squares estimator is the best linear unbiased estimator but following the kriging theory its use is limited. This paper shows the kriging constraint on the classic generalized least-squares estimator.

The adjustment of the binomial data by small constants is a common practice in statistical modelling, for avoiding sparseness issues and, historically, for improving the asymptotic properties of the estimators. However, there are two main disadvantages with such practice: i) there is not a universal constant adjustment that results estimators with optimal asymptotic properties for all possible ...