对大数据的期望意味着

IF 16.4 1区化学 Q1 CHEMISTRY, MULTIDISCIPLINARY Accounts of Chemical Research Pub Date : 2023-01-01 DOI:10.7153/jmi-2023-17-27

Tomislav Buric, N. Elezovic, Lenka Mihoković

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引用次数: 1

摘要

．本文给出了大数据功率均值期望的估计公式，并将其与概率分布均值和随机样本均值联系起来。该方法由幂均值的渐近展开式推导而来，适用于足够大的数据，尤其适用于难以获得期望值的情况。我们将展示这些近似对于均匀分布和正态分布的随机样本的准确性，并分析它们在大样本量下的行为。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Expectations of large data means

. In this paper we present estimation formulas for the expectations of power means of large data and associate them with means of probability distribution and means of random sample. The proposed method follows from the asymptotic expansion of power means which is applicable for suf ﬁ ciently large data and it is especially useful when value of such expectation is hard to obtain. We will show the accuracy of these approximations for random samples which have uniform and normal distribution and analyse their behaviour for large sample volume.

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来源期刊

Accounts of Chemical Research 化学-化学综合

CiteScore

31.40

自引率

1.10%

发文量

312

审稿时长

2 months

期刊介绍： Accounts of Chemical Research presents short, concise and critical articles offering easy-to-read overviews of basic research and applications in all areas of chemistry and biochemistry. These short reviews focus on research from the author’s own laboratory and are designed to teach the reader about a research project. In addition, Accounts of Chemical Research publishes commentaries that give an informed opinion on a current research problem. Special Issues online are devoted to a single topic of unusual activity and significance. Accounts of Chemical Research replaces the traditional article abstract with an article "Conspectus." These entries synopsize the research affording the reader a closer look at the content and significance of an article. Through this provision of a more detailed description of the article contents, the Conspectus enhances the article's discoverability by search engines and the exposure for the research.

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