Statistics of the longest interval in renewal processes
Abstract
We consider renewal processes where events, which can for instance be the zero crossings of a stochastic process, occur at random epochs of time. The intervals of time between events, , are independent and identically distributed (i.i.d.) random variables with a common density . Fixing the total observation time to induces a global constraint on the sum of these random intervals, which accordingly become interdependent. Here we focus on the largest interval among such a sequence on the fixed time interval . Depending on how the last interval is treated, we consider three different situations, indexed by I, II and III. We investigate the distribution of the longest interval and the probability that the last interval is the longest one. We show that if decays faster than for large , then the full statistics of is given, in the large limit, by the standard theory of extreme value statistics for i.i.d. random variables, showing in particular that the global constraint on the intervals does not play any role at large times in this case. However, if exhibits heavy tails, for large , with index , we show that the fluctuations of are governed, in the large limit, by a stationary universal distribution which depends on both and , which we compute exactly. On the other hand, is generically different from its counterpart for i.i.d. variables (both for narrow or heavy tailed distributions ). In particular, in the case , the large behaviour of gives rise to universal constants (depending also on both and ) which we compute exactly.
Keywords
Cite
@article{arxiv.1412.7381,
title = {Statistics of the longest interval in renewal processes},
author = {Claude Godreche and Satya N. Majumdar and Gregory Schehr},
journal= {arXiv preprint arXiv:1412.7381},
year = {2015}
}
Comments
30 pages, 9 figures, minor revisions