A general association index with applications to spatio-temporal data

ABSTRACT

We propose an association index for random variables that measures the
strength of their dependency and that is able to capture complex non-linear associations. We apply the index to several spatial and spatio-temporal problems, including association between space and time in point processes and preferential sampling in geostatistics. We show how to estimate our index avoiding the curse of dimensionality.