Synopsis "Long-Memory Time Series: Theory and Methods "
A self-contained, contemporary treatment of the analysis oflong-range dependent data Long-Memory Time Series: Theory and Methods provides an overviewof the theory and methods developed to deal with long-rangedependent data and describes the applications of thesemethodologies to real-life time series. Systematically organized,it begins with the foundational essentials, proceeds to theanalysis of methodological aspects (Estimation Methods, AsymptoticTheory, Heteroskedastic Models, Transformations, Bayesian Methods,and Prediction), and then extends these techniques to more complexdata structures.To facilitate understanding, the book:Assumes a basic knowledge of calculus and linear algebra andexplains the more advanced statistical and mathematicalconceptsFeatures numerous examples that accelerate understanding andillustrate various consequences of the theoretical resultsProves all theoretical results (theorems, lemmas, corollaries,etc.) or refers readers to resources with further demonstrationIncludes detailed analyses of computational aspects related tothe implementation of the methodologies described, includingalgorithm efficiency, arithmetic complexity, CPU times, andmoreIncludes proposed problems at the end of each chapter to helpreaders solidify their understanding and practice their skillsA valuable real-world reference for researchers andpractitioners in time series analysis, economerics, finance, andrelated fields, this book is also excellent for a beginninggraduate-level course in long-memory processes or as a supplementaltextbook for those studying advanced statistics, mathematics,economics, finance, engineering, or physics. A companion Web siteis available for readers to access the S-Plus and R data sets usedwithin the text.
