An important aspect of estimation theory is characterizing the best achievable performance in a given estimation problem, as well as determining estimators that achieve the optimal performance. The traditional Cramér-Rao type bounds provide benchmarks on the variance of any estimator of a deterministic parameter vector under suitable regularity conditions, while requiring a-priori specification...

Maximum likelihood methods are by far the most popular methods for deriving statistical estimators. However, parametric likelihoods require distributional specifications. The empirical likelihood is a nonparametric likelihood function that does not require such distributional assumptions, but is otherwise analogous to its parametric counterpart. Both likelihoods assume that the random variables...

The effects of DC offsets on four variations of the stochastic gradient algorithm are analyzed to determine the most appropriate algorithm for hardware implementation. The output mean squared error (MSE) performance in the presence of DC offsets is evaluated and compared with computer simulations for each of the algorithms assuming a Gaussian input distribution.

We discuss the problem of image restoration from observed images. Most of the image restoration filters proposed so far include parameters that control the restoration properties. For bringing out the optimal restoration performance of the filters, the parameter values should be determined so as to minimize a certain error measure such as the mean squared error (MSE) between the restored image ...

This module motivates and introduces the minimum variance unbiased estimator (MVUE). This is the primary criterion in the classical (frequentist) approach to parameter estimation. We introduce the concepts of mean squared error (MSE), variance, bias, unbiased estimators, and the bias-variance decomposition of the MSE. The Minimum Variance Unbiased Estimator 1 In Search of a Useful Criterion In ...

In the first part of paper, one-parameter (1P), fifth and seventh order polynomial interpolationconvolution kernels, are described. second paper an Experiment is The precision theimage interpolation was tested. Interpolation Test images from base, using wereperformed. kernels measured mean squared error (MSE). Next,optimum kernel parameter, α, were determined by minimizing MSE. After that, a co...

This chapter proposes a nonlinear artificial Higher Order Neural Network (HONN) model to study the relation between manager compensation and performance in the governmental sector. Using a HONN simulator, this study analyzes city manager compensation as a function of local government performance, and compares the results with those from a linear regression model. This chapter shows that the non...

The paper aims to create a most efficient and accurate cab fare prediction system using machine learning algorithms comparing them. are Random forest algorithm Linear regression the r-square, mean square error (MSE), Root MSE Mean Squared Logarithmic Error (RMSLE) values. We implement linear predict prices of get best accuracy when both algorithms. should be trips before starting trip. sample s...