Title: A Bayesian Zero-inflated Latent class Model for Longitudinal Data Abstract: This work focuses on developing latent class models for longitudinal data with zero-inflated count response variables. The goals are to model discrete, longitudinal patterns of counts of rare events (for instance, health-risky behavior), and to identify individual-specific covariates associated with latent class probabilities. Two discrete latent structures are present in this type of model: a latent categorical variable that classifies subgroups with distinct developmental trajectories and a latent binary... Read more about ResearchStats: Gavino Puggioni
"U.S. presidents must constantly make decisions on issues about which they are not expert. Accordingly, they must rely on others for information, analysis, options, assessments, and recommendations. How presidents approach this challenge has varied...
Title: Some Fundamental Ideas for Causal Inference on Large Networks
Abstract: Classical approaches to causal inference largely rely on the assumption of "lack of interference", according to which the outcome of each individual does not depend on the treatment assigned to others. In many applications, however, including healthcare interventions in schools, online education, and design of online auctions and political campaigns on social media, assuming lack of interference is untenable. In this talk, Prof. Airoldi will introduce some fundamental ideas to deal with interference in causal... Read more about StochStatMIT: Edo Airoldi