论文标题
使用模拟退火改善ERGM启动值
Improving ERGM Starting Values Using Simulated Annealing
论文作者
论文摘要
指数式家庭模型的估计理论的大部分是特殊情况，包括指数族家庭模型（ERGMS）是特殊的，尤其是最大的可能性估计值，尤其是许多理想的属性。但是，在许多ERGMS的情况下，不可能直接计算MLE，因此必须采用近似MLE和/或替代估计方法的方法。许多MLE近似方法需要替代性估计作为起点。我们在这里讨论一类此类替代方案。与MPLE不同，MLE满足了所谓的“似然原理”。这意味着，即使不同的网络具有相同的统计数据，它们也可能具有不同的样本。我们在此处利用这一事实来搜索改进的基于近似MLE方法的启动值。我们提出的方法表明了它在生产网络数据集和模型的MLE方面的优点，该网络数据集和模型使用所有其他已知方法违反了估计。
Much of the theory of estimation for exponential family models, which include exponential-family random graph models (ERGMs) as a special case, is well-established and maximum likelihood estimates in particular enjoy many desirable properties. However, in the case of many ERGMs, direct calculation of MLEs is impossible and therefore methods for approximating MLEs and/or alternative estimation methods must be employed. Many MLE approximation methods require alternative estimates as starting points. We discuss one class of such alternatives here. The MLE satisfies the so-called "likelihood principle," unlike the MPLE. This means that different networks may have different MPLEs even if they have the same sufficient statistics. We exploit this fact here to search for improved starting values for approximation-based MLE methods. The method we propose has shown its merit in producing an MLE for a network dataset and model that had defied estimation using all other known methods.
