New Technique to Estimate the Asymmetric Trimming Mean

Abstract EN

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.