On multivariate smoothed bootstrap consistency

Daniele De Martini; Fabio Rapallo

doi:10.1016/j.jspi.2007.06.035

On multivariate smoothed bootstrap consistency

Daniele De Martini
, Fabio Rapallo

Research output: Contribution to journal › Article › peer-review

Abstract

This paper deals with the convergence in Mallows metric for classical multivariate kernel distribution function estimators. We prove the convergence in Mallows metric of a locally orientated kernel smooth estimator belonging to the class of sample smoothing estimators. The consistency follows for the smoothed bootstrap for regular functions of the marginal means. Two simple simulation studies show how the smoothed versions of the bootstrap give better results than the classical technique.

Original language	English
Pages (from-to)	1828-1835
Number of pages	8
Journal	Journal of Statistical Planning and Inference
Volume	138
Issue number	6
DOIs	https://doi.org/10.1016/j.jspi.2007.06.035
Publication status	Published - 1 Jul 2008
Externally published	Yes

Keywords

Bootstrap confidence intervals
Locally adaptive kernel estimator
Mallows metric

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10.1016/j.jspi.2007.06.035

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title = "On multivariate smoothed bootstrap consistency",

abstract = "This paper deals with the convergence in Mallows metric for classical multivariate kernel distribution function estimators. We prove the convergence in Mallows metric of a locally orientated kernel smooth estimator belonging to the class of sample smoothing estimators. The consistency follows for the smoothed bootstrap for regular functions of the marginal means. Two simple simulation studies show how the smoothed versions of the bootstrap give better results than the classical technique.",

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AU - De Martini, Daniele

AU - Rapallo, Fabio

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Y1 - 2008/7/1

N2 - This paper deals with the convergence in Mallows metric for classical multivariate kernel distribution function estimators. We prove the convergence in Mallows metric of a locally orientated kernel smooth estimator belonging to the class of sample smoothing estimators. The consistency follows for the smoothed bootstrap for regular functions of the marginal means. Two simple simulation studies show how the smoothed versions of the bootstrap give better results than the classical technique.

AB - This paper deals with the convergence in Mallows metric for classical multivariate kernel distribution function estimators. We prove the convergence in Mallows metric of a locally orientated kernel smooth estimator belonging to the class of sample smoothing estimators. The consistency follows for the smoothed bootstrap for regular functions of the marginal means. Two simple simulation studies show how the smoothed versions of the bootstrap give better results than the classical technique.

KW - Bootstrap confidence intervals

KW - Locally adaptive kernel estimator

KW - Mallows metric

UR - https://www.scopus.com/pages/publications/39449120447

U2 - 10.1016/j.jspi.2007.06.035

DO - 10.1016/j.jspi.2007.06.035

M3 - Article

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VL - 138

SP - 1828

EP - 1835

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

IS - 6

ER -

On multivariate smoothed bootstrap consistency

Abstract

Keywords

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