A uniqueness result for L-estimators, with applications to L-moments

Q Mathematics Statistical Methodology Pub Date : 2015-05-01 DOI:10.1016/j.stamet.2014.08.002
J.R.M. Hosking , N. Balakrishnan
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引用次数: 8

Abstract

We show that if a linear combination of expectations of order statistics has mean zero across all random variables that have finite mean, then the linear combination is identically zero. A consequence of this result is that any functional of a probability distribution can have essentially only one unbiased L-estimator (i.e., an estimator that has the form of a linear combination of order statistics): if two such linear combinations have the same expectation then they must be algebraically identical. We use this result to prove the equivalence of two statistics that have been proposed as estimators of the L-moments introduced by Hosking (1990), and to provide alternative means of computing estimators of the trimmed L-moments introduced by Elamir and Seheult (2003). We also make comparisons of the speed of various methods for computing estimators of L-moments and trimmed L-moments.

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l估计量的唯一性结果,及其在l矩上的应用
我们证明,如果有序统计量期望的线性组合在所有具有有限均值的随机变量上的平均值为零,则该线性组合等于零。这个结果的一个推论是,概率分布的任何泛函本质上只能有一个无偏l估计量(即,一个具有有序统计量线性组合形式的估计量):如果两个这样的线性组合具有相同的期望,那么它们必须在代数上相同。我们利用这一结果证明了霍斯金(1990)引入的l -矩估计量的两个统计量的等价性,并提供了计算Elamir和Seheult(2003)引入的修整l -矩估计量的替代方法。我们还比较了计算l -矩估计量和裁剪l -矩估计量的各种方法的速度。
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来源期刊
Statistical Methodology
Statistical Methodology STATISTICS & PROBABILITY-
CiteScore
0.59
自引率
0.00%
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0
期刊介绍: Statistical Methodology aims to publish articles of high quality reflecting the varied facets of contemporary statistical theory as well as of significant applications. In addition to helping to stimulate research, the journal intends to bring about interactions among statisticians and scientists in other disciplines broadly interested in statistical methodology. The journal focuses on traditional areas such as statistical inference, multivariate analysis, design of experiments, sampling theory, regression analysis, re-sampling methods, time series, nonparametric statistics, etc., and also gives special emphasis to established as well as emerging applied areas.
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