Results 261 to 270 of about 24,206 (303)

Higher — order accuracy of bootstrap for smooth functionals

Lecture Notes in Statistics, 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.
Enno Mammen, Mammen Enno
exaly   +3 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.
Daniele De Martini
exaly   +2 more sources

A recentred bootstrap procedure for constructing uniformly correct confidence sets under smooth function models [PDF]

Statistics, 2017
It has been found, under a smooth function model setting, that the n out of n bootstrap is inconsistent at stationary points of the smooth function, but that the m out of n bootstrap is consistent, provided that a correct convergence rate is specified of
Zhuqing Yu
exaly   +2 more sources

Smoothed Bootstrap Methods for Bivariate Data

Journal of Statistical Theory and Practice, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Asamh Saleh M. Al Luhayb, Tahani Coolen-Maturi, Frank P. A. Coolen   +2 more
openaire   +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
openaire   +2 more sources

On the smoothed bootstrap

Journal of Statistical Planning and Inference, 2000
Let \(X_1,X_2,\dots, X_n\) be a sample of independent and identically distributed random variables. Denote by \(F\) the distribution function of \(X\). The authors consider the problem of estimating the parameter \(T(F)\) by a statistic \(T(\widehat F_n)\), where \(\widehat F_n(x)= \int^x_{-\infty} f_n(u) du\) and \(f_n(u)\) is the well-known kernel ...
El-Nouty, Charles, Guillou, Armelle
openaire   +1 more source

On the bootstrap and smoothed bootstrap

Communications in Statistics - Theory and Methods, 1989
The standard bootstrap and two commonly used types of smoothed bootstrap are investigated. The saddlepoint approximations are used to evaluate the accuracy of the three bootstrap estimates of the density of a sample mean. The optimal choice for the smoothing parameter is obtained when smoothing is useful in reducing the mean squared error.
openaire   +1 more source

On smoothed bootstrap for density functionals

Journal of Nonparametric Statistics, 2003
We analyze, from both theoretical and practical point of view, the use of the smoothed bootstrap in the estimation of a functional T(f) of the underlying density. We consider a plug-in approach based on the use of an estimator of type T(fˆ n ) where fˆ n is a nonparametric (kernel) estimator of f.
Andrés M. Alonso, Antonio Cuevas
openaire   +1 more source

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.
openaire   +1 more source

Two-sample smooth test for the equality of distributions for dependent data and its bootstrap consistency [PDF]

Electronic Journal of Statistics
Various approaches have been proposed for the two-sample and the k-sample problem. This includes, for instance, characteristic function based approaches, empirical likelihood methods, kernel based approaches, so-called smooth tests which are based on ...
Eric Beutner
exaly   +2 more sources