Long-memory, or more generally fractal, processes are known to play an important role in
many scientific disciplines and applied fields such as physics, geophysics, hydrology …

The familiar Gaussian models do not allow for large deviations and are thus often
inadequate for modeling high variability. Non-Gaussian stable models do not possess such …

This book is about stochastic-process limits-limits in which a sequence of stochastic
processes converges to another stochastic process. These are useful and interesting …

This book is concerned with extreme value theory for stochastic processes whose finite-
dimensional distributions are heavy-tailed in the restrictive sense of regular variation. These …

This book serves as a standard reference, making this area accessible not only to
researchers in probability and statistics, but also to graduate students and practitioners. The …

This volume is devoted to the study of asymptotic properties of wide classes of stochastic
systems arising in mathematical statistics, percolation theory, statistical physics and …

The aim of this paper is to provide conditions which ensure that the affinely transformed
partial sums of a strictly stationary process converge in distribution to an infinite variance …

Under an appropriate regular variation condition, the affinely normalized partial sums of a
sequence of independent and identically distributed random variables converges weakly to …

In the paper, we continue to investigate measures of dependence for random variables with
infinite variance. For random variables with regularly varying tails, we introduce a general …

A p-stable limit theorem holds for partial sums Sn of a stationary sequence, if S nB n→ gμ for
some 1/p-regularly varying sequence and some non-denegerate strictly p-stable law μ. The …