Detecting a Conditional Extreme Value Model
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 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.