Optimal Choices for Trimming in Trimmed L-moment Method
نویسندگان
چکیده
Trimmed Linear moments (TL-moments) are natural generalization of Lmoments that do not require the mean of the underlying distribution to exist. It is known that the sample TL-moments is unbiased estimators to corresponding population TL-moment. Since different choices for the amount of trimming give different variances for the estimators it is important to choose the estimator that has less variance than others. Therefore, we derive an optimal choice for the amount of trimming from known distributions based on the minimum sum of the absolute value of the errors between the quantile probability function and its TLmoments representation. Moreover, we study simulation-based approach to choose an optimal amount of trimming by computing the estimator variance for range of trimming and choose the one which has less variance. Several examples are given to show the benefits of the methods.
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