Conditioned limit laws constitute an important and well developed framework of extreme value theory that describe a broad range of extremal dependence forms including asymptotic independence. We explore the assumption of conditional independence of X1 and X2 given X0 and study its implication in the limiting distribution of (X1;X2) conditionally on X0 being large. We show that under random norming, conditional independence is always preserved in the conditioned limit law but might fail to do so when the normalisation does not include the precise value of the random variable in the conditioning event.
|Journal||Statistics and Probability Letters|
|Early online date||14 Jan 2016|
|Publication status||Published - May 2016|