标题： 潜扩散-观测采纳模型的修剪估计量 显示英文标题

标题： A Trimming Estimator for the Latent-Diffusion-Observed-Adoption Model

摘要： 网络扩散模型适用于许多经济社会互动，然而网络交互难以观察或测量。每当扩散过程未被观测时，潜在网络矩阵（捕捉代理的扩散状态）的可能实现数量会随着网络规模呈指数增长。由于相互依赖性，对数似然函数不能分解为独立组件。因此，超过一轮交互的潜在扩散模型的精确估计在计算上是不可行的。在本文中，我提出了一种修剪估计器，使我能建立并最大化一个近似的对数似然函数，在不超过三分之一符合条件的代理受到修剪的情况下，该函数几乎完全识别出真实对数似然函数的峰值。 显示英文摘要

摘要： Network diffusion models are applicable to many socioeconomic interactions, yet network interaction is hard to observe or measure. Whenever the diffusion process is unobserved, the number of possible realizations of the latent matrix that captures agents' diffusion statuses grows exponentially with the size of network. Due to interdependencies, the log likelihood function can not be factorized in individual components. As a consequence, exact estimation of latent diffusion models with more than one round of interaction is computationally infeasible. In the present paper, I propose a trimming estimator that enables me to establish and maximize an approximate log likelihood function that almost exactly identifies the peak of the true log likelihood function whenever no more than one third of eligible agents are subject to trimming.