Abstract Although ionosphere-weighted GNSS parameter estimation is a popular technique for strengthening estimator performance in the presence of ionospheric delays, no provable rules yet exist that specify needed weighting dependence on circumstances. The goal present contribution therefore to develop and conditions need be satisfied order solution mean squared error (MSE) superior ionosphere-...

Stochastic linear programs can be solved approximately by drawing a subset of all possible random scenarios and solving the problem based on this subset, an approach known as sample path optimization. The value of the optimal solution to the sampled problem provides an estimate of the true objective function value. This estimator is known to be optimistically biased; the expected optimal object...

We propose a general maximum likelihood empirical Bayes (GMLEB) method for the estimation of a mean vector based on observations with iid normal errors. We prove that under mild moment conditions on the means, the average mean squared error (MSE) of the GMLEB is within an infinitesimal fraction of the minimum average MSE among all separable estimators which use a single deterministic estimating...

This paper defines the preliminary test estimator (PTE) of the univariate normal mean under the original as well as the Edgeworth size corrected Wald (W), likelihood ratio (LR) and Lagrange multiplier (LM) tests. The bias and mean squared error (MSE) functions of the estimators are derived. The conflicts among the biases and the MSEs of the PTEs under the three original and the size corrected t...

Agencies and policy makers are interested in constructing reliable estimates for areas with small sample sizes, where areas often refer to geographic areas and demographic groups. The estimation for such areas is known as small area estimation. Procedures based on models have been used to construct estimates for the small area means, by exploiting auxiliary information. Mixed models are suitabl...

In this paper, a relationship between linear discriminant analysis (LDA) and the generalized minimum squared error (MSE) solution is presented. The generalized MSE solution is shown to be equivalent to applying a certain classification rule in the space defined by LDA. The relationship between the MSE solution and Fisher discriminant analysis is extended to multiclass problems and also to under...

We address the problem of computing IMSE (Integrated Mean-Squared Error) optimal designs for random fields interpolation with known mean and covariance. We both consider the IMSE and truncated-IMSE (approximation of the IMSE by spectral truncation). We assume that the MSE is integrated through a discrete measure and restrict the design space to the support of the considered measure. The IMSE an...

In this paper we present an adaptive BlockBased EigenVector Algorithm (BBEVA) for blind equalization of time-varying multipath fading channels. In addition we assess the performance of the new algorithm for different configurations and compare the results with the least mean squares (LMS) algorithm. The new algorithm is evaluated in terms of intersymbol interference (ISI) suppression, mean squa...

In this paper, we propose improved versions of normalized least mean square (NLMS) algorithm: single and multiple -variable step size normalized least mean square (VSSNLMS) algorithms for echo cancellation. The presented algorithms exhibit faster convergence rate in comparison to NLMS algorithm. Simulation results employing standard figure of merits show how the algorithms perform better than N...

We propose an efficiently structured nonlinear finitememory filter for denoising (filtering) a Gaussian signal contaminated by additive impulsive colored noise. The noise is modeled as a zero-mean Gaussian mixture (ZMGM) process. We first derive the optimal estimator for the static case, in which a Gaussian random variable (RV) is contaminated by an impulsive ZMGM RV. We provide an analytical d...