While the conditional mean is known to provide the minimum mean square error (MSE) forecast – and hence is optimal under a squared-error loss function – it must often in practice be replaced by a noisy estimate when model parameters are estimated over a small sample. Here two results are obtained, both of which motivate the use of forecasts biased toward zero (shrinkage forecasts) in such setti...

Tomlinson-Harashima precoding, design the transmit signal so that the ISI in the receiver side is very minimum. In this precoding moving the feedback filter to the transmit side to avoid error propagation problem. In this paper depth analysis of zero forcing (ZF) and minimum mean squared error (MMSE) equalizers applied to multi-input multi-output (MIMO) systems. We investigate the bit error rat...

This paper discusses new approaches to parameter estimation of gamma distribution based on representative points. In the first part, existence and uniqueness mean squared error points (MSE-RPs) are discussed theoretically. second by comparing three types points, we show that MSE-RPs perform well in simulation. The last part proposes a Harrel–Davis sample standardization technique. Simulation st...

Previous research demonstrated that a genetic algorithm (GA) can utilize supercomputers to evolve image compression and reconstruction transforms that reduce mean squared error (MSE) by more than 22% (1.126 dB) under conditions subject to quantization, while continuing to average the same amount of compression as the Daubechies-4 (D4) wavelet. This paper describes subsequent research that exten...

In this paper, we study the performance of estimators of parametersof two-parameter exponential distribution based on upper records. The generalized likelihood ratio (GLR) test was used to generate preliminary test estimator (PTE) for both parameters. We have compared the proposed estimator with maximum likelihood (ML) and unbiased estimators (UE) under mean-squared error (MSE) and Pitman me...

Normally distributed data arises in various contexts and often one is interested in estimating its variance. The authors limit themselves in this chapter to the class of estimators that are (positive) multiples of sample variances. Two important qualities of estimators are bias and variance, which respectively capture the estimator’s accuracy and precision. Apart from the two classical estimato...

In this article, we study the optimal structure of the source precoding matrix and the relay amplifying matrices for multiple-input multiple-output (MIMO) relay communication systems with parallel relay nodes. Two types of receivers are considered at the destination node: (1) The linear minimal mean-squared error (MMSE) receiver; (2) The nonlinear decision feedback equalizer based on the minima...

In this article, challenges regarding the provision of channel state information (CSI) in non-contiguous orthogonal frequency division multiplexing (NC-OFDM) cognitive radio (CR) systems are addressed. We propose a novel scheme that utilizes cross entropy (CE) optimization together with an analytical pilot power distribution technique to design pilot symbols that minimizes the channel estimate ...

We introduce a regression-based nested Monte Carlo simulation method for the estimation of financial risk. An outer simulation level is used to generate financial risk factors and an inner simulation level is used to price securities and compute portfolio losses given risk factor outcomes. The mean squared error (MSE) of standard nested simulation converges at the rate k−2/3, where k measures c...

In recent years, many approaches to differentially privately publish histograms have been proposed. Several approaches rely on constructing tree structures in order to decrease the error when answer large range queries. In this paper, we examine the factors affecting the accuracy of hierarchical approaches by studying the mean squared error (MSE) when answering range queries. We start with one-...