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

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