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首页> 外文期刊>Bernoulli: official journal of the Bernoulli Society for Mathematical Statistics and Probability >A unifying framework for k-statistics, polykays and their multivariate generalizations
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A unifying framework for k-statistics, polykays and their multivariate generalizations

机译:k统计量,多变量及其多元概括的统一框架

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摘要

Through the classical umbral calculus, we provide a unifying syntax for single and multivariate k-statistics, polykays and multivariate polykays. From a combinatorial point of view, we revisit the theory as exposed by Stuart and Ord, taking into account the Doubilet approach to symmetric functions. Moreover, by using exponential polynomials rather than set partitions, we provide a new formula for k-statistics that results in a very fast algorithm to generate such estimators.
机译:通过经典的本影演算,我们为单变量k统计量和多元k统计量,多元和多元多元提供了统一的语法。从组合的角度来看,我们考虑到对称函数的Doubilet方法,重新研究了Stuart和Ord公开的理论。此外,通过使用指数多项式而不是集合分区,我们为k统计量提供了一个新公式,该公式可以非常快速地生成此类估计量的算法。

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