Results 161 to 170 of about 88,029 (195)

Smooth bootstrap methods for analysis of longitudinal data

Statistics in Medicine, 2007
AbstractIn analysis of longitudinal data, the variance matrix of the parameter estimates is usually estimated by the ‘sandwich’ method, in which the variance for each subject is estimated by its residual products. We propose smooth bootstrap methods by perturbing the estimating functions to obtain ‘bootstrapped’ realizations of the parameter estimates ...
Li, Yue, Wang, You-Gan
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Bootstrap methods in regression smoothing^∗

Journal of Nonparametric Statistics, 1993
A new smoothed bootstrap resampling plan is introduced in this paper in the context of nonparametric regression smoothing. A study of the rates of convergence for this method is carried out in a similar way to that made in Cao-Abad (1991) for the normal approximation, its plug-in approach and the wild bootstrap.
R. Cao-abad, W. González-Manteiga
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Smoothed bootstrap confidence intervals with discrete data

Computational Statistics & Data Analysis, 1997
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Guerra, Rudy, Polansky, Alan M., Schucany, William R.   +2 more
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Smoothed Empirical Processes and the Bootstrap

2003
Based on a uniform functional central limit theorem (FCLT) for unbiased smoothed empirical processes indexed by a class.F of measurable functions defined on a linear metric space we present a consistency theorem for smoothed bootstrapped empirical processes.
Peter Gaenssler, Daniel Rost
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Bootstrapped and smoothed classification error rate estimators

Communications in Statistics - Simulation and Computation, 1988
The resubstitution estimator of classification error rates is known to have both an optimistic bias and a large variance. Modifications to this method have addressed these problems. the bootstrap estimator, for example, uses a resampling scheme to reduce bias, and the NS method uses a smoothing algorithm to reduce variance.
Steven M. Snapinn, James D. Knoke
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Bandwidth selection for the smoothed bootstrap percentile method

Computational Statistics & Data Analysis, 2001
Some applications of the bootstrap involve smoothing the estimated distribution that is resampled, a method known as the smoothed bootstrap. Recently, the effect of resampling a kernel smoothed distribution was evaluated through expansions for the coverage of bootstrap percentile confidence intervals.
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A Note on the Smoothed Bootstrap

2003
In this paper we treat the smoothed bootstrap based on histogram induced empirical measure. We demonstrate the superiority of this type of bootstrap in a very general sense. Moreover, we show that this bootstrap can effectively estimate the bias inherent from the histogram density estimation.
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What happens when bootstrapping the smoothing spline

Communications in Statistics - Theory and Methods, 1987
The usual smoothing spline method is modified by a bootstrap bias correction inserted. It is shown that such a modification is asymptotically equivalent to a higher order method in sense they share the same best obtainable mean square error convergence rate. Similar results about the kernel method and their relation are discussed. It turns out that all
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Higher — order accuracy of bootstrap for smooth functionals

1992
We come now back to bootstrap of smooth statistical functionals. In Chapter 1 we have studied under which conditions bootstrap of smooth functionals works. In this chapter we give a simple proof for the higher order accuracy of the bootstrap estimate for smooth functionals T.
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ASYMPTOTICS OF BOOTSTRAPPING MEAN ON SOME SMOOTHED EMPIRICAL DISTRIBUTION

Statistics & Risk Modeling, 1997
Summary: We considered bootstrapping the mean in the case where the unknown underlying distribution \(F\) is known to be continuous. Instead of resampling from the empirical distribution \(F_n\) formed from the given random sample \(\{X_1,\dots,X_n\}\), where \(X_i{\overset\text{i.i.d.}\sim}F\), we draw bootstrap samples from a smoothed empirical ...
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