Results 241 to 250 of about 9,169 (255)

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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A Smooth Block Bootstrap for Statistical Functionals and Time Series

Journal of Time Series Analysis, 2015
Unlike with independent data, smoothed bootstraps have received little consideration for time series, although data smoothing within resampling can improve bootstrap approximations, especially when target distributions depend on smooth population quantities (e.g., marginal densities).
Gregory, Karl B., Lahiri, Soumendra N., Nordman, Daniel J.   +2 more
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The choice of smoothing parameter in nonparametric regression through Wild Bootstrap

Computational Statistics & Data Analysis, 2004
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Wenceslao González-Manteiga, María Dolores Martínez Miranda, Ana Pérez González   +2 more
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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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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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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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Applications of Smoothed Monotone Regression Splines and Smoothed Bootstrapping in Survival Analysis

1998
An estimate of a survival curve S(x) for censored data is given by the non-parametric Kaplan-Meier method, which provides an estimate of the empirical survival curve and estimators of the standard errors. We use the software package Confit, developed by WTI, which is designed to produce a smoothed approximating spline, subject to imposed constraints on
Donald E. Ramirez, Philip W. Smith
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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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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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Weak Convergence of Smoothed and Nonsmoothed Bootstrap Quantile Estimates

Annals of Probability, 1989
R -D Reiss
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