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Bootstrapping through discrete convolutional methods.

Authors :
Clark, Jared M.
Warr, Richard L.
Source :
Applied Stochastic Models in Business & Industry; Jan2024, Vol. 40 Issue 1, p144-160, 17p
Publication Year :
2024

Abstract

Bootstrapping was designed to randomly resample data from a fixed sample using Monte Carlo techniques. However, the original sample itself defines a discrete distribution. Convolutional methods are well suited for discrete distributions, and we show the advantages of utilizing these techniques for bootstrapping. The discrete convolutional approach can provide exact numerical solutions for bootstrap quantities, or at least mathematical error bounds. In contrast, Monte Carlo bootstrap methods can only provide confidence intervals which converge slowly. Additionally, for some problems the computation time of the convolutional approach can be dramatically less than that of Monte Carlo resampling. This article provides several examples of bootstrapping using the proposed convolutional technique and compares the results to those of the Monte Carlo bootstrap, and to those of the competing saddlepoint method. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
15241904
Volume :
40
Issue :
1
Database :
Complementary Index
Journal :
Applied Stochastic Models in Business & Industry
Publication Type :
Academic Journal
Accession number :
175520521
Full Text :
https://doi.org/10.1002/asmb.2809