Multiple imputation is a statistical method for analyzing data with missing values. Nonparametric Markov chain bootstrap methods can be used to generate multiple imputations of both scalar and multivariate outcome variables, under the assumption that the data are missing completely at random, and nonparametric inference can be obtained using multiple implementation bootstrap. The nonparametric ...

In this paper we investigate the applicability of reliable and fast bootstrap methods on two non-smoothing, consistent specification tests (Bierens, 1982; Escanciano, 2006). Through Monte Carlo experiments we compare the performance of these tests under the null when the null distribution is simulated by the bootstrap, the double bootstrap (Beran, 1988) and the fast double bootstrap (FDB, David...

This paper discusses EM algorithm and Bootstrap approach combination applied for the improvement of the satellite image fusion process. This novel satellite image fusion method based on estimation theory EM algorithm and reinforced by Bootstrap approach was successfully implemented and tested. The sensor images are firstly split by a Bayesian segmentation method to determine a joint region map ...

Abstract: The article proposes a computationally efficient procedure for bias adjustment in the iterated bootstrap. The new technique replaces the need for successive levels of bootstrap resampling by proposing an approximation for the double bootstrap “calibrating coefficient” using only one draw from the second level probability distribution. Extensive Monte Carlo evidence suggest that the pr...

This paper proposes a new, e cient, gure from ground discrimination method. This algorithm is based on the assumption that background data features can be more easily detected than gure data features, thus emphasizing the background detection task (and implying the name of the method). Along the iterative labeling process, data features are sequentially and permanently labelled as "background",...

We propose a bootstrap sampling method jackboot sampling This provides more accu rate inferences than ordinary bootstrap sampling better con dence interval coverage and less biased or unbiased standard errors The method is simple to implement We also prescribe a smoothing parameter for use in smoothed bootstrapping using or dinary kernel smoothing The e ect is similar to that of jackboot sampling

In this paper, we propose a model-free bootstrap method for the empirical process under absolute regularity. More precisely, consistency of an adapted version of the so-called dependent wild bootstrap, that was introduced by Shao (2010) and is very easy to implement, is proved under minimal conditions on the tuning parameter of the procedure. We apply our results to construct confidence interva...