We consider estimating a random vector from its measurements in a fusion frame, in presence of noise and subspace erasures. A fusion frame is a collection of subspaces, for which the sum of the projection operators onto the subspaces is bounded below and above by constant multiples of the identity operator. We first consider the linear minimum mean-squared error (LMMSE) estimation of the random...

ABSTRACT—We investigate a pilot-symbol based channel estimation scheme that uses a combination of Maximum-Likelihood (ML) estimation and Wiener filtering. Besides the optimum Wiener filter, we also investigate suboptimum Wiener filters which offer a better robustness against path delay time estimation errors and/or have a lower complexity. The mean squared error (MSE) of the channel impulse res...

The performance of the Constant Modulus Algorithm can su er because of the existence of local minimawith large Mean Squared Error(MSE). This paper presents a new way of obtaining the optimumMSE over all delays using a second equalizer under a mixed Constant Modulus and Cross Correlation Algorithm (CM-CCA). Proof of convergence is obtained for the noiseless case. Simulations demonstrate the pote...

Bayesian approximate message passing (BAMP) is an efficient method in compressed sensing that is nearly optimal in the minimum mean squared error (MMSE) sense. Bayesian approximate message passing (BAMP) performs joint recovery of multiple vectors with identical support and accounts for correlations in the signal of interest and in the noise. In this paper, we show how to reduce the complexity ...

Several empirical equations to estimate coal pillar strength have been presented in academic studies. The development processes of these equations are similar and are usually obtained by fitting the mathematical function (curve) to field data. One of the best criteria to evaluate the quality of fitting for such equations is the correlation coefficient R2, which has limited applicability. It is ...

In this paper, we propose a new two-parameter biased estimator in gamma regression models when there is collinearity among the regressors. We investigate mean squared error (MSE) properties of newly proposed estimator. Moreover, provide some theorems to compare estimators existing ones. conduct Monte Carlo simulation study under different designs sense MSE. real data application show usefulness...

This paper presents the synchronization between uncertain hyperchaotic and chaotic systems. Based on Lyapunov stability theory, an adaptive controller is derived to achieve synchronization of hyperchaotic and chaotic systems, including the case of unknown parameters in these two systems. The T.N.Č. hyperchaotic oscillator is used as the master system, and the Rössler system is used as the slave...

The problem of parameter estimation in linear model is pervasive in signal processing and communication applications. It is often common to restrict attention to linear estimators, which simplifies the implementation as well as the mathematical derivations. The simplest design scenario is when the second order statistics of the parameters to be estimated are known and it is desirable to minimiz...

We examine the role of nontraded goods in the city real exchange rate changes in India. Using the Mean Squared Error (MSE) decomposition we …nd that nontraded goods explain about 30% of the variation of the Indian city real exchange rate changes, rather than the small amount found in other cross-country studies. We also analyze the role of consumption elasticity of substitution between traded a...

In this communication, sample measures of kurtosis adapted by various software packages are compared for data from normal and non-normal populations. Further, two improved estimators of population kurtosis are proposed and their performance is compared with the currently used measures. The suggested estimators have considerably lower mean squared error (MSE) for various sampling designs in our ...