Tail Inference: where does the tail begin?
The quality of estimation of tail parameters, such as tail index in
the univariate case, or the spectral measure in the multivariate case,
depends crucially on the part of the sample included in the estimation.
A simple approach involving sequential statistical testing is proposed
in order to choose this part of the sample. This method can be used
both in the univariate and multivariate cases. It is computationally
efficient, and can be easily automated. No visual inspection of the
data is required. We establish consistency of the Hill estimator when
used in conjunction with the proposed method, as well describe its
asymptotic fluctuations. We compare our method to existing methods in
univariate and multivariate tail estimation, and use it to analyze
Danish fire insurance data.
grant W911NF-07-1-0078, NSF grant DMS-1005903 and NSA grant
H98230-11-1-0154 at Cornell University.