New Technique to Estimate the Asymmetric Trimming Mean
A trimming mean eliminates the extreme observations by removing observations from each end of the ordered sample. In this paper, we adopted the Hogg's and Brys's tail weight measures. In addition, a new algorithm was proposed as a linear estimator based on the quartile; we used a quartile...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2010-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2010/739154 |
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| Summary: | A trimming mean eliminates the extreme observations by removing observations from each end of the ordered sample. In this paper, we adopted the Hogg's and Brys's tail weight measures. In addition, a new algorithm was proposed as a linear estimator based on the quartile; we used a quartile to divide the data into three and four groups. Then two new estimators were proposed. These classes of linear estimators were examined via simulation method over a variety of asymmetric distributions. Sample sizes 50, 100, 150, and 200 were generated using R program. The results of 50 were tabulated, since we have similar results for the other sizes. These results were tabulated for 7 asymmetric distributions with total trimmed proportions 0.10 and 0.20 on both sides, respectively. The results for these estimators were ordered based on their relative efficiency. |
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| ISSN: | 1687-952X 1687-9538 |