Sparse spatially clustered coefficient model via adaptive regularization.

Large spatial datasets with many spatial covariates have become ubiquitous in many fields in recent years. A question of interest is to identify which covariates are likely to influence a spatial response, and whether and how the effects of these covariates vary across space, including potential abrupt changes from region to region. To solve this question, a new efficient regularized spatially clustered coefficient (RSCC) regression approach is proposed, which could achieve variable selection and identify latent spatially heterogeneous covariate effects with clustered patterns simultaneously.

Two Wrongs Make a Right: Addressing Underreporting in Binary Data from Multiple Sources.

Media-based event data-i.e., data comprised from reporting by media outlets-are widely used in political science research.