An Improvement to Interval Estimation for Small Samples
Because it is difficult and complex to determine the probability distribution of small samples，it is improper to use traditional probability theory to process parameter estimation for small samples. Bayes Bootstrap method is always used in the project. Although，the Bayes Bootstrap method has its own...
| Main Authors: | , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Harbin University of Science and Technology Publications
2017-02-01
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| Series: | Journal of Harbin University of Science and Technology |
| Subjects: |
| Summary: | Because it is difficult and complex to determine the probability distribution of small samples，it is improper to use traditional probability theory to process parameter estimation for small samples. Bayes Bootstrap method is always used in the project. Although，the Bayes Bootstrap method has its own limitation，In this article an improvement is given to the Bayes Bootstrap method，This method extended the amount of samples by numerical simulation without changing the circumstances in a small sample of the original sample. And the new method can give the accurate interval estimation for the small samples. Finally，by using the Monte Carlo simulation to model simulation to the specific small sample problems. The effectiveness and practicability of the Improved-Bootstrap method was proved. |
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| ISSN: | 1007-2683 |