A. Mitra and S. Resnick

Hidden regular variation defines a subfamily of distributions satisfying multivariate regular variation on [0, \infty]^d \backslash \{(0,0, \cdots, 0) \} and models another regular variation on the sub-cone with the axes removed. We extend this concept to other subcones in higher dimensions, suggest a procedure for detecting the presence of hidden regular variation, and if it exists, propose a method of estimating the limit measure exploiting its semi-parametric structure. We exhibit examples where use of hidden regular variation yields better estimates of probabilities of risk sets.