Simulation evaluation of small samples based on grey estimation and improved bootstrap
Grey Systems: Theory and Application
ISSN: 2043-9377
Article publication date: 18 February 2021
Issue publication date: 28 February 2022
Abstract
Purpose
To solve the problem of simulation evaluation with small samples, a fresh approach of grey estimation is presented based on classical statistical theory and grey system theory. The purpose of this paper is to make full use of the difference of data distribution and avoid the marginal data being ignored.
Design/methodology/approach
Based upon the grey distribution characteristics of small sample data, the definition about a new concept of grey relational similarity measure comes into being. At the same time, the concept of sample weight is proposed according to the grey relational similarity measure. Based on the new definition of grey weight, the grey point estimation and grey confidence interval are studied. Then the improved Bootstrap resampling is designed by uniform distribution and randomness as an important supplement of the grey estimation. In addition, the accuracy of grey bilateral and unilateral confidence intervals is introduced by using the new grey relational similarity measure approach.
Findings
The new small sample evaluation method can realize the effective expansion and enrichment of data and avoid the excessive concentration of data. This method is an organic fusion of grey estimation and improved Bootstrap method. Several examples are used to demonstrate the feasibility and validity of the proposed methods to illustrate the credibility of some simulation data, which has no need to know the probability distribution of small samples.
Originality/value
This research has completed the combination of grey estimation and improved Bootstrap, which makes more reasonable use of the value of different data than the unimproved method.
Keywords
Acknowledgements
This work is supported by the National Natural Science Foundation of China (11801173 and 11702094), and the national key research project (2018YFC0808306). The study is also supported by Key Discipline of Probability Theory and Mathematical Statistics of North China Institute of Science and Technology (06DV09), and Hebei IoT Monitoring Engineering Technology Research Center (3142018055).
Citation
Yang, W., Lin, L. and Gao, H. (2022), "Simulation evaluation of small samples based on grey estimation and improved bootstrap", Grey Systems: Theory and Application, Vol. 12 No. 2, pp. 376-388. https://doi.org/10.1108/GS-09-2020-0121
Publisher
:Emerald Publishing Limited
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