An accurate estimate of the uncertainty associated with a parameter estimate is important if we want to avoid misleading inference. The bootstrap technique (Efron, 1979; Efron and Tisbshirani, 1993) is a very general way of measuring the accuracy of estimators, and was originally developed for parameter estimation given independent identically distributed (i.i.d.) data. However, random effects ...

where f is a non linear continuous function, ( ) dt t t x x , , 1 K = x is a vector of d non stochastic explanatory variables defined on a compact א⊂R, and { } t Z is a stationary noise process with zero mean. The function f in the model (1) can be approximated with a single hidden layer feed-forward neural network; Hornik et al. (1989) have shown that this class of non linear functions can app...

We consider the problem of estimating the local accuracy of image registration when no ground truth data is available. The technique is based on a statistical resampling technique called bootstrap. Only the two input images are used, no other data are needed. The general bootstrap uncertainty estimation framework described here is in principle applicable to most of the existing pixel based regi...

In this paper, one novel approach for text detection in video frames, which is based on bootstrap artificial neural network (BANN) and CED operator, is proposed. This method first uses a new color image edge operator (CED) to segment the image and achieve the elementary candidate text block. And then the neural network is introduced into the further classification of the text blocks and the non...

This article applies a novel bootstrap method, the kernel block (KBB), to quasi-maximum likelihood (QML) estimation of dynamic models with stationary strong mixing data. The method first weights components comprising quasi-log function in an appropriate way and then samples resultant transformed using standard ‘m out n’ bootstrap. We investigate first-order asymptotic properties KBB for QML dem...

In this paper, we study the performance of the bootstrap channel estimation scheme, which consists in this paper in using the hard-decided outputs of the channel decoder in order to extend the training sequence. Based on a simple large system analysis, we derive the expression of the channel estimation MSE in order to evaluate the impact of using wrong decisions on channel estimation performanc...

In recentyears,severalnewparametricand nonparametricbootstrapmethods have been proposed for time seriesdata. Which of these methods should applied researchersuse? We provide evidence that for many applicationsin time serieseconometricsparametricmethods are more accurate,and we identify directionsfor future researchon improving nonparametricmethods. We explicitly addressthe important, but often ...

In analyzing data from a survey, researchers often need to compare the effectiveness of several logistic regression models. The receiver operating characteristic curve offers one way to measure effectiveness of prediction, by calculating the area under the curve (AUC). We present a SAS macro for calculating AUC that takes the survey weights into account. For comparing logistic regression models...