Lognormal linear models are widely used in applications, and many times it is of interest to predict the response variable at the original scale for a new set of covariate values. In this paper we consider the problem of efficient estimation of the conditional mean of the response variable at the original scale for lognormal linear models. Several existing estimators are reviewed, including the...

We discuss the prediction of a spatial variable of a multivariate mark composed of both dependent and explanatory variables. The marks are location-dependent and they are attached to a point process. We assume that the marks are assigned independently, conditionally on an unknown underlying parametric field. We compare (i) the classical non-parametric Nadaraya–Watson kernel estimator based on t...

The Shewhart and the Bonferroni-adjustment S control charts are usually applied to monitor the standard deviation of a quality characteristic. The control limits of these charts are constructed using approximately the normal distribution in case that the standard deviation parameter is known or unknown. In this paper, we establish a new S chart that is based approximately on the normal distribu...

This paper deals with the estimation of the stress-strength parameter R = P (Y < X) when X and Y are independent Lindley random variables with different shape parameters. The uniformly minimum variance unbiased estimator has explicit expression, however, its exact or asymptotic distribution is very difficult to obtain. The maximum likelihood estimator of the unknown parameter can also be obtain...

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, ...

A classical problem in statistics is estimating the expected coverage of a sample, which has had applications in gene expression, microbial ecology, optimization, and even numismatics. Here we consider a related extension of this problem to random samples of two discrete distributions. Specifically, we estimate what we call the dissimilarity probability of a sample, i.e., the probability of a d...

In most situations the best estimator of a function of the parameter exists, but sometimes it has a complex form and we cannot compute its variance explicitly. Therefore, a lower bound for the variance of an estimator is one of the fundamentals in the estimation theory, because it gives us an idea about the accuracy of an estimator. It is well-known in statistical inference that the Cram&eac...

In this paper, we propose an unbiased minimum variance es-timator to estimate the parameters of an ellipse. The objective of the optimization is to compute a minimum variance estimator. The experimental results show the dramatic improvement over existed weighted least sum of squares approach especially when the ellipse is occluded.