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Background: Mass spectrometry based quantification of peptides can be performed using the iTRAQ™ reagent in conjunction with mass spectrometry. This technology yields information about the relative abundance of single peptides. A method for the calculation of reliable quantification information is required in order to obtain biologically relevant data at the protein expression level.

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We introduce some mathematical framework for functional extreme value theory and provide basic definitions and tools. In particular we introduce a functional domain of attraction approach for stochastic processes, which is more general than the usual one based on weak convergence.The distribution function G of a continuous max-stable process on [0, 1] is introduced and it is shown that G can be represented via a norm on functional space, called D-norm. This is in complete accordance with the multivariate case and leads to the definition of functional generalized Pareto distributions (GPD) W . These satisfy W = 1+log(G) in their upper tails, again in complete accordance with the uni-or multivariate case.Applying this framework to copula processes we derive characterizations of the domain of attraction condition for copula processes in terms of tail equivalence with a functional GPD.δ-neighborhoods of a functional GPD are introduced and it is shown that these are characterized by a polynomial rate of convergence of functional extremes, which is well-known in the multivariate case.1991 Mathematics Subject Classification. Primary 60G70.