Estimating the residual variance is an important question in nonparametric regression. Among the existing estimators, the optimal difference-based variance estimation proposed in Hall, Kay, and Titterington (1990) is widely used in practice. Their method is restricted to the situation when the errors are independent and identically distributed. In this paper, we propose the optimal difference-b...

The great potential of \foveated imaging" lies in the entropy reduction relative to the original image while minimizing the loss of visual information. Utilizing human foveation combined with video compression, as well as communication and human-machine interface techniques, more e cient multimedia services are expected to be provided in the near future. In this paper, we introduce a prototype ...

A novel method to improve the yield gradient estimation in parametric yield optimization is proposed. By introducing some deterministic information into the conventional Monte Carlo method and fully utilizing the samples, it is possible to obtain yield gradient estimation with significantly smaller variance. The additional computation is almost negligible. Examples are presented to indicate the...

In this paper, a multi-sensor information fusion steady-state Kalman estimator for discrete time stochastic linear systems with system errors and sensor errors is presented. Gevers-Wouters(G-W) algorithm is used in this paper. Steady-state Kalman estimator is presented in this paper avoids the complex Diophantine equation, and it is based on the ARMA model to compute the steady-state Kalman est...

Variance estimation for the regression estimator for a two-phase sample is investigated. A replication variance estimator with number of replicates equal to or slightly larger than the size of the second-phase sample is developed. In surveys where the second-phase sample is much smaller than the first-phase sample, the procedure has practical advantages. The method is similar in spirit to one o...

For sequential output data analysis in non-terminating discrete-event simulation, we consider three methods of point and interval estimation of the steady-state variance. We assess their performance in the analysis of the output of queueing simulations by means of experimental coverage analysis. Over a range of models, estimating variances turns out to involve considerably more observations tha...

In this paper we study the impact of variance estimation on a receiver, where the soft information provided by the channel decoder is utilized in the multiuser detection. Specifically, we consider a receiver structure that resembles the structure of an iterative decoder for a serially concatenated code, but where the inner constituent decoder is replaced by an interference cancellation (IC) uni...

We study a variance reduction technique for Monte Carlo estimation of functionals in Markov chains. The method is based on designing sequential control variates using successive approximations of the function of interest V . Regular Monte Carlo estimates have a variance of O(1/N), where N is the number of sample trajectories of the Markov chain. Here, we obtain a geometric variance reduction O(...

This paper addresses the problem of segmenting a time-series with respect to changes in the mean value or in the variance. The first case is when the time data is modeled as a sequence of independent and normal distributed random variables with unknown, possibly changing, mean value but fixed variance. The main assumption is that the mean value is piecewise constant in time, and the task is to ...

Two-phase sampling is often used in a wide variety of surveys. Variance estimation from a two-phase sample has been a subject of active research. The re-sampling method of variance estimation has been used for this problem. However, the method confronts a challenging problem when the first phase sampling fraction is high. In the extreme (but not uncommon) case some first-phase strata are take-a...