Estimation of the time-average variance constant (TAVC) of a stationary process plays a fundamental role in statistical inference for the mean of a stochastic process. Wu (2009) proposed an efficient algorithm to recursively compute the TAVC with O(1) memory and computational complexity. In this paper, we propose two new recursive TAVC estimators that can compute TAVC estimate with O(1) computa...

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 ...

Three novel adaptive multichannel L-filters based on marginal data ordering are proposed. They rely on well-known algorithms for the unconstrained minimization of the Mean Squared Error (MSE), namely, the Least Mean Squares (LMS), the normalized LMS (NLMS) and the LMS-Newton (LMSN) algorithm. Performance comparisons in color image filtering have been made both in RGB and U∗V ∗W ∗ color spaces. ...

We introduce a new analysis of an adaptive mixture method that combines outputs of two constituent filters running in parallel to model an unknown desired signal. This adaptive mixture is shown to achieve the mean square error (MSE) performance of the best constituent filter, and in some cases outperforms both, in the steady-state. However, the MSE analysis of this mixture in the steady-state a...

The problem of estimating a random signal vector x observed through a linear transformation H and corrupted by an additive noise is considered. A linear estimator that minimizes the mean squared error (MSE) with a certain selected probability is derived under the assumption that both the additive noise and random signal vectors are zero mean Gaussian with known covariance matrices. Our approach...

To gain insight into various phenomena of interest, cumulative distribution functions (CDFs) can be used to analyze survey data. The purpose this study was present an efficient ratiocum-exponential estimator for estimating a population CDF using auxiliary information under two scenarios non-response. Up first-order approximation, expressions the bias and mean squared error (MSE) were derived. p...

This paper describes a method that minimizes the mean squared error (MSE) in estimating the spherical harmonic components of the surface air temperature field. The ratio of the MSE to the variance of the spherical harmonic component is expressed in terms of the length scale Xo, and the positions and weights of the measurement stations. The weights are optimized by the condition of minimizing th...

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...

Introduction Multivariate Fay–Herriot models for estimating small area indicators are introduced. Among the available procedures for fitting linear mixed models, the residual maximum likelihood (REML) is employed. The empirical best predictor (EBLUP) of the vector of area means is derived. An approximation to the matrix of mean squared crossed prediction errors (MSE) is given and four MSE estim...