Spatial/Spatiotemporal interdependence - i.e., that the outcomes, actions, or choices of some unit-times depend on those of others - is substantively and theoretically ubiquitous and central in binary outcomes of interest across the social sciences. However, most empirical applications omit...

One of the central challenges to inference in the context of potentially interdependent observations, known as Galton's Problem, is the difficulty distinguishing spatially correlated observations due to observed units exposure to spatially correlated shocks from spatial correlation in outcomes...

Although scholars recognize that time-series-cross-section data typically correlate across both time and space, they tend to model temporal dependence directly, often by lags of dependent variables, but to address spatial interdependence solely as a nuisance to be "corrected" by FGLS or to which...

Assume we have a dataset, Z say, from the joint distribution of random variables X and Y , and two further, independent datasets, X and Y, from the marginal distributions of X and Y , respectively. We wish to combine X, Y and Z, so as to construct an estimator of the joint density. This problem...