The problem we are seeking to solve is the continual estimation of a set of parameters whose values change over time. Updating is achieved by combining a set of observations or measurements z(t) which contain information about the signal of interest x(t). The role of the estimator is to provide an estimate x̂(t+ ) at some time t+ . If > 0 we have a prediction filter, if < 0 a smoothing filter an...

When properly constructed, biased estimators are known to produce lower mean-square errors than unbiased estimators. A biased estimator for the problem of ultrasound time-delay estimation was recently proposed. The proposed estimator incorporates knowledge of adjacent displacement estimates into the final estimate of a displacement. This is accomplished by using adjacent estimates to create a p...

Rousseeuw’s minimum covariance determinant (MCD) method is a highly robust estimator of multivariate mean and covariance. In practice, the MCD covariance estimator may be singular. However, a nonsingular covariance estimator is required to calculate the Mahalanobis distance. In order to fix this singular problem, we propose an improved version of the MCD estimator, which is a combination of the...

We propose a new unbiased estimator for estimating the utility of optimal stopping problem. The MUSE, short Multilevel Unbiased Stopping Estimator, constructs Monte Carlo (MLMC) at every stage problem in backward recursive way. In contrast to traditional sequential methods, MUSE can be implemented parallel. prove has finite variance, computational complexity, and achieves $\epsilon$-accuracy wi...

The state estimation problem is studied for networked control systems (NCSs) subject to random communication delays and the measurements without time stamps. With the random delay bounded by one step only, a new measurement model is proposed for possible out-of-sequence measurements. For unstable systems, to guarantee linearly unbiased estimator and uniformly bounded estimation error variance, ...

The ratio-type estimators have been introduced for estimating the mean and total population, but in recent years based on the ratio methods several estimators for population variance have been proposed. In this paper two families of estimators have been suggested and their approximation mean square error (MSE) have been developed. In addition, the efficiency of these variance estimators are com...

The maximum likelihood estimator of the Weibull shape parameter can be very biased. An estimator based on the modified profile likelihood is proposed and its properties are studied. It is shown that the new estimator is almost unbiased with relative bias being less than 1% in most of situations, and it is much more efficient than the regular MLE. The smaller the sample or the heavier of the cen...

‎we extend the method of adaptive two-stage sequential sampling to‎‎include designs where there is more than one criteria is used in‎‎deciding on the allocation of additional sampling effort‎. ‎these‎‎criteria‎, ‎or conditions‎, ‎can be a measure of the target‎‎population‎, ‎or a measure of some related population‎. ‎we develop‎‎murthy estimator for the design that is unbiased estimators for‎‎t...

Abstract: Recently, supervised artificial neural networks have obtained success to reveal and provide quantitative information concerning defects in TNDE (Thermographic NonDestructive Evaluation). Supervised neural networks may converge to local minimum and their training procedure are usually long. In this study, a neuro-fuzzy approach is applied to characterize subsurface defects in TNDE. Sim...