严重程度异质性下的最优网络成员估计

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-08-13 DOI:10.1080/01621459.2024.2388903

Zheng Tracy Ke, Jingming Wang

引用次数: 0

摘要

真实网络通常具有严重的度异质性，最大、平均和最小节点度相差很大。本文研究了节点度异质性对统计结果的影响。

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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Optimal Network Membership Estimation under Severe Degree Heterogeneity

Real networks often have severe degree heterogeneity, with maximum, average, and minimum node degrees differing significantly. This paper examines the impact of degree heterogeneity on statistical ...

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.