对随机多图模型的适合性测试的良好性
1GESIS - Leibniz Institute for the Social Sciences, Cologne, Germany.
Journal of applied statistics
|November 16, 2023
概括
本研究介绍了两种概率多图模型的适合性测试的好处:随机断片匹配 (RSM) 和独立边缘赋值 (IEA). 模拟显示这些测试准确地近似分布,有助于社交网络分析.
科学领域:
- 图形理论是指图形的理论.
- 统计建模 统计建模
- 网络分析 网络分析
背景情况:
- 概率的多图形模型对于分析复杂网络至关重要.
- 现有的方法缺乏对依赖边缘分配的稳健的适合性测试.
研究的目的:
- 开发和评估两个概率多图模型的适合性测试的优点:随机结尾匹配 (RSM) 和独立边缘赋值 (IEA).
- 用统计测量和模拟来评估这些测试的性能.
主要方法:
- 根据边缘复数序列的基础上,拟合测试的制定质量.
- 使用了皮尔森型和概率比率测试统计数据.
- 根据不同的模型,为Pearson统计学推导出预期值.
- 进行模拟以评估测试性能和分布近似值.
主要成果:
- 测试统计数据的零分布通过非对称的奇平方分布得到很好的近似,即使边缘很少.
- 非零分布可以通过调整的千平方分布来近似进行功率分析.
- 随机断片匹配与小边数的独立边缘分配相比,显著地改变了测试统计分布.
结论:
- 拟合测试的良性是对概率多图模型有效的.
- 这些测试为零和非零分布提供了可靠的近似值.
- 这些方法为分析社交网络结构和发现潜在的网络形成过程提供了有价值的工具.
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