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 …

Monte Carlo techniques, including MCMC and other methods, are widely used and generate
sets of samples from a parameter space of interest that can be used to infer or plot quantities …

Amongmanyexcitingdevelopmentsinstatistic…, nonlineartimeseriesanddata-
analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In …

This is a book on bootstrap and related resampling methods for temporal and spatial data
exhibiting various forms of dependence. Like the resam pling methods for independent data …

Markov Chain Monte Carlo (MCMC) methods are now an indispensable tool in scientific
computing. This book discusses recent developments of MCMC methods with an emphasis …

Two oft‐cited drawbacks of kernel density estimators (KDEs) of home range are their
sensitivity to the choice of smoothing parameter (s) and their need for independent data …

The formal concept of an animal's home range, or derivations thereof, has been around for
over half a century (Burt 1943). Within this time frame there have been countless published …

Heavy-tailed distributions are typical for phenomena in complex multi-component systems
such as biometry, economics, ecological systems, sociology, web access statistics, internet …

This book offers a predominantly theoretical coverage of statistical prediction, with some
potential applications discussed, when data and/or parameters belong to a large or infinite …

In Bayesian inference, predictive distributions are typically in the form of samples generated
via Markov chain Monte Carlo or related algorithms. In this paper, we conduct a systematic …