分形维数一致最小方差无偏估计

Q4 Multidisciplinary
Zeny L. Maureal, Elmer Castillano, Roberto Padua
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引用次数: 0

摘要

本文用幂律分布引入了分形分布的概念。通过对指数分布的分形随机变量进行对数变换,确定分形与指数分布之间的关系。研究了分数维的点估计量及其统计特性。证明了分数维λ的极大似然估计是有偏的。利用Lehmann-Scheffe定理发现并证明了另一个估计量是一致最小方差无偏估计量(UMVUE)。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Uniform Minimum Variance Unbiased Estimator of Fractal Dimension
The paper introduced the concept of a fractal distribution using a power-law distribution. It proceeds to determining the relationship between fractal and exponential distribution using a logarithmic transformation of a fractal random variable which turns out to be exponentially distributed. It also considered finding the point estimator of fractional dimension and its statistical characteristics. It was shown that the maximum likelihood estimator of the fractional dimension λ is biased. Another estimator was found and shown to be a uniformly minimum variance unbiased estimator (UMVUE) by Lehmann-Scheffe’s theorem.
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来源期刊
CiteScore
0.70
自引率
0.00%
发文量
19
审稿时长
8 weeks
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