  In classical extreme value theory probabilities of extreme events are
estimated assuming all the components of a random vector to be in a domain of
attraction of an extreme value distribution. In contrast, the conditional
extreme value model assumes a domain of attraction condition on a
sub-collection of the components of a multivariate random vector. This model
has been studied in
\cite{heffernan:tawn:2004,heffernan:resnick:2007,das:resnick:2008a}.
  In this paper we propose three statistics which act as tools to detect this
model in a bivariate set-up. In addition, the proposed statistics also help to
distinguish between two forms of the limit measure that is obtained in the
model.
