Fusion Method for True Value Estimation Based on Information Poor Theory
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| Title | Fusion Method for True Value Estimation Based on Information Poor Theory |
| Authors | |
| Abstract | Poor information means incomplete and insufficient information, such as small sample and unknown distribution. For point estimation under the condition of poor information, the statistical methods relied on large sample sizes and known distributions may become ineffective. For this end, a fusion method is proposed. The fusion method develops five methods, three concepts, and one rule. The five methods include the rolling mean method, the membership function method, the maximum membership grade method, the moving bootstrap method, and the arithmetic mean method. The three concepts comprise the solution set on the estimated true value, the fusion series, and the final estimated true value. The rule is the range rule. The results of the Monte Carlo simulation and of the experimental investigation on information of the quality evaluation for the tapered roller bearing indicate that the fusion method allows the number of the data to be little and the distribution to be unknown, having the reliable estimated result. |
| Publisher | ACADEMY PUBLISHER |
| Date | 2012-02-01 |
| Source | Journal of Computers Vol 7, No 2 (2012) |
| Rights | Copyright © ACADEMY PUBLISHER - All Rights Reserved.To request permission, please check out URL: http://www.academypublisher.com/copyrightpermission.html. |