Long-Memory Time Series / Wiley Series in Probability and Statistics (PDF)

A self-contained, contemporary treatment of the analysis of
long-range dependent data

Long-Memory Time Series: Theory and Methods provides an overview
of the theory and methods developed to deal with long-range
dependent data and describes the applications of these
methodologies to real-life time series. Systematically organized,
it begins with the foundational essentials, proceeds to the
analysis of methodological aspects (Estimation Methods, Asymptotic
Theory, Heteroskedastic Models, Transformations, Bayesian Methods,
and Prediction), and then extends these techniques to more complex
data structures.

To facilitate understanding, the book:

* Assumes a basic knowledge of calculus and linear algebra and
explains the more advanced statistical and mathematical
concepts

* Features numerous examples that accelerate understanding and
illustrate various consequences of the theoretical results

* Proves all theoretical results (theorems, lemmas, corollaries,
etc.) or refers readers to resources with further demonstration

* Includes detailed analyses of computational aspects related to
the implementation of the methodologies described, including
algorithm efficiency, arithmetic complexity, CPU times, and
more

* Includes proposed problems at the end of each chapter to help
readers solidify their understanding and practice their skills

A valuable real-world reference for researchers and
practitioners in time series analysis, economerics, finance, and
related fields, this book is also excellent for a beginning
graduate-level course in long-memory processes or as a supplemental
textbook for those studying advanced statistics, mathematics,
economics, finance, engineering, or physics. A companion Web site
is available for readers to access the S-Plus and R data sets used
within the text.