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During the last decades long memory processes have evolved as a vital and important part of time series analysis. This book attempts to give an overview of the theory and methods developed to deal with long range dependent data as well as describe some applications of these methodologies to real life time series.
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(r) and R data sets used within the text.
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(r) and R data sets used within the text.
"...an interested reader should be able to acquire a decent grounding in the subject area covered." (International Statistical Review, 2007)
"...Palma presents a textbook for a graduate course summarizing the theory and methods developed to deal with long-range-dependent data, and describing some applications to real-life time series." (SciTech Book Reviews, June 2007)
"...textbook for a graduate course summarizing the theory and methods developed to deal with long-range-dependent data, and describing some applications to real-life time series.... Problems and bibliographic notes are provided at the end of each chapter." (SciTech Book News, June 2007)
"I believe that this text provides an important contribution to the long-memory time series literature. I feel that it largely achieves its aims and could be useful for those instructors wishing to teach a semester-long special topics course.... I strongly recommend this book to anyone interested in long-memory time series. Both researchers and beginners alike will find this text extremely useful." (Journal of the American Statisticial Association, Dec 2008)
"Very well-organized catalogue of long-memory time series analysis." (Mathematical Reviews, 2008)
"Judging by its contents and scope [the aim of this book] has been largely achieved.... The list of references is selective but quite comprehensive. Each chapter concludes with a 'Problems' section which should be helpful to instructors wishing to use this book as standalone basis for a course in its subject area..." (International Statistical Review, 2007)
"...textbook for a graduate course summarizing the theory and methods developed to deal with long-range-dependent data, and describing some applications to real-life time series.... Problems and bibliographic notes are provided at the end of each chapter." (SciTech Book News, June 2007)
"I believe that this text provides an important contribution to the long-memory time series literature. I feel that it largely achieves its aims and could be useful for those instructors wishing to teach a semester-long special topics course.... I strongly recommend this book to anyone interested in long-memory time series. Both researchers and beginners alike will find this text extremely useful." (Journal of the American Statisticial Association, Dec 2008)
"Very well-organized catalogue of long-memory time series analysis." (Mathematical Reviews, 2008)
"Judging by its contents and scope [the aim of this book] has been largely achieved.... The list of references is selective but quite comprehensive. Each chapter concludes with a 'Problems' section which should be helpful to instructors wishing to use this book as standalone basis for a course in its subject area..." (International Statistical Review, 2007)