英文文献：Testing for Stochastic Dominance in Social Networks-测试社交网络中的随机优势
英文文献作者：Firmin Doko Tchatoka,Robert Garrard,Virginie Masson
英文文献摘要：
This paper illustrates how stochastic dominance criteria can be used to rank social networks in terms of efficiency, and develops statistical inference procedures for assessing these criteria. The tests proposed can be viewed as extensions of a Pearson goodness-of-fit test and a studentized maximum modulus test often used to partially rank income distributions and inequality measures. We establish uniform convergence of the empirical size of the tests to the nominal level, and show their consistency under the usual conditions that guarantee the validity of the approximation of a multinomial distribution to a Gaussian distribution. Furthermore, we propose a bootstrap method that enhances the finite-sample properties of the tests. The performance of the tests is illustrated via Monte Carlo experiments and an empirical application to risk sharing networks in rural India.

本文说明如何随机优势标准可用于排名社会网络的效率，并发展统计推断程序，以评估这些标准。所提出的检验可以看作是皮尔森拟合优度检验和研究的最大模量检验的扩展，最大模量检验通常用于收入分布和不等式测度的部分排序。在保证多项式分布逼近高斯分布有效性的一般条件下，我们建立了检验的经验规模在名义水平上的一致收敛，并证明了它们的一致性。此外，我们提出了一种bootstrap方法来增强测试的有限样本性质。通过蒙特卡洛实验和印度农村风险分担网络的实证应用，说明了测试的性能。