Affiliation:
1. School of Automotive Engineering, Shanghai University of Engineering Science, Shanghai, China
Abstract
The bootstrap method is mostly used to estimate statistical characteristics of small sample data. However, the limitations of the bootstrap method itself lead to a reduction in the reliability of small-sample estimates. In this article, an improved bootstrap method is developed to address this problem. In the statistically significant error range (the sample average error and the limit error of sampling) of the original single sample data, expanding the virtual test data that obey two distributions to overcome the limitations of the bootstrap method itself. This article compares and analyses these two methods through the case; the result indicates that the improved bootstrap method can enhance the reliability of the estimation results without changing its probability distribution. We also discussed how to reduce the fluctuation of the improved bootstrap method. And the effectiveness and feasibility of this improved method are discussed in the analysis of fatigue life test data.
Funder
National Natural Science Foundation of China
Subject
Mechanical Engineering,Mechanics of Materials,General Materials Science,Computational Mechanics
Cited by
40 articles.
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