Extended Abstract. It is a traditional way in biological, sociological, agricultural and geological studies to partition a geographical area into quadrats and then take a sample of them by a particular sampling design. We study the relevant characteristic of quadrats to estimate a parameter of the population. We suppose that the variable of interest has a positive spatial autocorrelation. Sampl...

the aim of this study was to estimate suspended sediment by the ann model, dt with cart algorithm and different types of src, in ten stations from the lorestan province of iran. the results showed that the accuracy of ann with levenberg-marquardt back propagation algorithm is more than the two other models, especially in high discharges. comparison of different intervals in models showed that r...

Abstract The Uniformly Minimum Variance Unbiased (UMVU) and the Maximum Likelihood (ML) estimations of R = P ( X ≤ Y ) associated variance are considered for independent discrete random variables Y. Assuming a uniform distribution as member one parameter exponential family distributions, theoretical expressions such quantities derived. Similar obtained when interchange their roles both from dis...

This comment corrects several errors found in the paper, “Class of Cyclic-Based Estimators for Frequency-Offset Estimation of OFDM Systems.” In addition, we show that the minimum variance unbiased estimator for frequency offset derived in the above paper is the maximum-likelihood estimator when the timing delay is perfectly known. I. SYSTEM MODEL F irst, the probability density function (pdf) o...

The minimum error entropy (MEE) criterion has been receiving increasing attention due to its promising perspectives for applications in signal processing and machine learning. In the context of Bayesian estimation, the MEE criterion is concerned with the estimation of a certain random variable based on another random variable, so that the error’s entropy is minimized. Several theoretical result...

In this paper, a bias-compensated least squares (BCLS) method in the closed loop environment is proposed. It is assumed that the observation noise is a white gaussinan signal while there are no process noises. It is also assumed that the plant is controlled by a linear time invariant controller and that the closed loop system is asymptotically stable. The proposed estimator is unbiased and it d...

We derive an unbiased variance estimator for re-sampling procedures using the fact that those procedures are incomplete U-statistics. Our approach is based on careful examination of the combinatorics governing the covariances between re-sampling iterations. We establish such an unbiased variance estimator for the special case of K-Fold cross-validation. This estimator exists as soon as new obse...

In this paper, the moving horizon recursive state estimator for linear singular systems is derived from the minimum variance estimation problem. The proposed estimate of the state using the measured outputs samples on the recent finite time horizon is unbiased and independent of any a priori information of the state on the horizon. The convergence and stability of the filter are evoked. A numer...

We consider the best quadratic unbiased estimators of the integrated variance in the presence of independent market microstructure noise. We establish the asymptotic normality of a feasible best quadratic unbiased estimator under the assumption of constant volatility and show that it is asymptotically e cient when the market microstructure noise is normal. Since the class of quadratic estimator...

Gene diversity, or expected heterozygosity (H), is a common statistic for assessing genetic variation within populations. Estimation of this statistic decreases in accuracy and precision when individuals are related or inbred, due to increased dependence among allele copies in the sample. The original unbiased estimator of expected heterozygosity underestimates true population diversity in samp...