A general association index with applications to spatio-temporal data
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.