Stochastic processes are widely used for model building in the social, physical, engineering
and life sciences as well as in financial economics. In model building, statistical inference for …

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 …

Statistical Methods for Long Term Memory Processes covers the diverse statistical methods
and applications for data with long-range dependence. Presenting material that previously …

This paper provides a survey and review of the major econometric work on long memory
processes, fractional integration, and their applications in economics and finance. Some of …

Traffic modeling of today's communication networks is a prime example of the role statistical
inference methods for stochastic processes play in such classical areas of applied …

This paper provides a bibliographical guide to researchers and tra c engineers who are
interested in self-similar tra c modeling and analysis. It lists some of the most recent network …

Our study of fractal landscapes departs from the simplest but yet effective model of fractional
Brownian motion and explores its two-dimensional (2-D) extensions. We focus on the ability …

Comprehensive theoretical overview of kernel smoothing methods with motivating examples
Kernel smoothing is a flexible nonparametric curve estimation method that is applicable …

Long-range dependence is often observed in long time series. Correlations decay
approximately like| k| 2H-2, with H ε (0.5, 1), as the lag k tends to infinity. The long-term …

This paper proposes two simple tests that are based on certain time domain properties of I
(d) processes. First, if a time series follows an I (d) process, then each subsample of the time …