Results 231 to 240 of about 1,306,091 (274)

A smoothed bootstrap estimator for a studentized sample quantile

Annals of the Institute of Statistical Mathematics, 1993
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D. Janas
semanticscholar   +2 more sources

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.
D. Radulovic
semanticscholar   +2 more sources

Smoothed bootstrap consistency through the convergence in mallows metric of smooth estimates

Journal of Nonparametric Statistics, 2000
This article examines smoothed bootstrap consistency of the sample mean, regular functions of the sample mean and multiple regression models. We use Mallows metric convergence of the empirical distribution, plugged-in by the bootstrap method, towards the theoretical distribution of the data.
D. Martini
semanticscholar   +3 more sources

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.
P. Gaenssler, D. Rost
semanticscholar   +2 more sources

Smoothing the Bootstrap

International Statistical Review / Revue Internationale de Statistique, 1992
Summary: The question of smoothing when using the non-parametric version of the bootstrap for estimation of population functionals is reconsidered. In general, there is no global preference for procedures based on a smoothed version of the empirical distribution rather than the empirical distribution itself.
de Angelis, Daniela, Young, G. Alastair
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The bootstrap: To smooth or not to smooth?

Biometrika, 1987
In the statistical bootstrap the sample is resampled with replacement. However, these samples with replacement are not drawn from the unknown distribution F itself but from the empirical distribution function \(F_ n\) of the observed data. Because \(F_ n\) is a discrete distribution, samples constructed from \(F_ n\) in the bootstrap simulations will ...
Silverman, B. W., Young, G. A.
openaire   +1 more source

Inference via kernel smoothing of bootstrap values

Computational Statistics & Data Analysis, 2007
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Racine, Jeffrey S., MacKinnon, James G.
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Histospline smoothing the Bayesian bootstrap

Biometrika, 1988
Summary: This paper describes a version of the Bayesian bootstrap that assigns random Dirichlet mass uniformly across statistically equivalent blocks. The method requires prior knowledge that the underlying distribution is continuous with known compact support.
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Local Smoothing Using the Bootstrap

Communications in Statistics - Simulation and Computation, 2013
We consider the problem of data-based choice of the bandwidth of a kernel density estimator, with an aim to estimate the density optimally at a given design point. The existing local bandwidth selectors seem to be quite sensitive to the underlying density and location of the design point.
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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
openaire   +3 more sources