Large deviations of the maximum of independent and identically distributed random variables

Abstract

A pedagogical account of some aspects of Extreme Value Statistics (EVS) is presented from the somewhat non-standard viewpoint of Large Deviation Theory. We address the following problem: given a set of $N$ i.i.d. random variables $\{X_1,\dots ,X_N\}$ drawn from a parent probability density function (pdf) $p(x)$, what is the probability that the maximum value of the set $X_{\max }={\max }_i{X}_i$ is "atypically larger" than expected? The cases of exponential and Gaussian distributed variables are worked out in detail, and the right rate function for a general pdf in the Gumbel basin of attraction is derived. The Gaussian case convincingly demonstrates that the full rate function cannot be determined from the knowledge of the limiting distribution (Gumbel) alone, thus implying that it indeed carries additional information. Given the simplicity and richness of the result and its derivation, its absence from textbooks, tutorials and lecture notes on EVS for physicists appears inexplicable.

14 pag., 1 fig. - Accepted for publication in European Journal of Physics

References in corpus (6)

• The large deviation approach to statistical mechanics
• Large Deviations of Extreme Eigenvalues of Random Matrices
• Extreme Value Statistics of Eigenvalues of Gaussian Random Matrices
• Large Deviations of the Maximum Eigenvalue for Wishart and Gaussian Random Matrices
• Extreme value problems in Random Matrix Theory and other disordered systems
• Renormalization flow in extreme value statistics

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