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Statistical Methods for Long Term Memory Processes covers the diverse statistical methods and applications for data with long-range dependence. Presenting material that previously appeared only in journals, the author provides a concise and effective overview of probabilistic foundations, statistical methods, and applications. The material emphasizes basic principles and practical applications and provides an integrated perspective of both theory and practice. This book explores data sets from a wide range of disciplines, such as hydrology, climatology, telecommunications engineering, and high-precision physical measurement. The data sets are conveniently compiled in the index, and this allows readers to view statistical approaches in a practical context.
Statistical Methods for Long Term Memory Processes also supplies S-PLUS programs for the major methods discussed. This feature allows the practitioner to apply long memory processes in daily data analysis. For newcomers to the area, the first three chapters provide the basic knowledge necessary for understanding the remainder of the material. To promote selective reading, the author presents the chapters independently. Combining essential methodologies with real-life applications, this outstanding volume is and indispensable reference for statisticians and scientists who analyze data with long-range dependence.
Traditionally, statistics and music are not generally associated with each other. However, "intelligent" music software, computer digitization, and other advanced techniques and technologies have precipitated the need for standard statistical models to answer basic musicological questions. Statistics In Musicology presents an unprecedented introduction to statistical and mathematical methods developed for use in music analysis, music theory, and performance theory. It explores concrete methods for data generation and numerical encoding of musical data and serves as a practical reference for a wide audience, including statisticians, mathematicians, musicologists, and musicians.
This book provides a concise introduction to the mathematical
foundations of time series analysis, with an emphasis on
mathematical clarity. The text is reduced to the essential logical
core, mostly using the symbolic language of mathematics, thus
enabling readers to very quickly grasp the essential reasoning
behind time series analysis. It appeals to anybody wanting to
understand time series in a precise, mathematical manner. It is
suitable for graduate courses in time series analysis but is
equally useful as a reference work for students and researchers
alike.
Long-memory processes are known to play an important part in many
areas of science and technology, including physics, geophysics,
hydrology, telecommunications, economics, finance, climatology, and
network engineering. In the last 20 years enormous progress has
been made in understanding the probabilistic foundations and
statistical principles of such processes. This book provides a
timely and comprehensive review, including a thorough discussion of
mathematical and probabilistic foundations and statistical methods,
emphasizing their practical motivation and mathematical
justification. Proofs of the main theorems are provided and data
examples illustrate practical aspects. This book will be a valuable
resource for researchers and graduate students in statistics,
mathematics, econometrics and other quantitative areas, as well as
for practitioners and applied researchers who need to analyze data
in which long memory, power laws, self-similar scaling or fractal
properties are relevant.
The purpose of this book is to establish a connection between the
traditional field of empirical economic research and the emerging
area of empirical financial research and to build a bridge between
theoretical developments in these areas and their application in
practice. Accordingly, it covers broad topics in the theory and
application of both empirical economic and financial research,
including analysis of time series and the business cycle; different
forecasting methods; new models for volatility, correlation and of
high-frequency financial data and new approaches to panel
regression, as well as a number of case studies. Most of the
contributions reflect the state-of-art on the respective subject.
The book offers a valuable reference work for researchers,
university instructors, practitioners, government officials and
graduate and post-graduate students, as well as an important
resource for advanced seminars in empirical economic and financial
research.
The purpose of this book is to establish a connection between the
traditional field of empirical economic research and the emerging
area of empirical financial research and to build a bridge between
theoretical developments in these areas and their application in
practice. Accordingly, it covers broad topics in the theory and
application of both empirical economic and financial research,
including analysis of time series and the business cycle; different
forecasting methods; new models for volatility, correlation and of
high-frequency financial data and new approaches to panel
regression, as well as a number of case studies. Most of the
contributions reflect the state-of-art on the respective subject.
The book offers a valuable reference work for researchers,
university instructors, practitioners, government officials and
graduate and post-graduate students, as well as an important
resource for advanced seminars in empirical economic and financial
research.
Long-memory processes are known to play an important part in many
areas of science and technology, including physics, geophysics,
hydrology, telecommunications, economics, finance, climatology, and
network engineering. In the last 20 years enormous progress has
been made in understanding the probabilistic foundations and
statistical principles of such processes. This book provides a
timely and comprehensive review, including a thorough discussion of
mathematical and probabilistic foundations and statistical methods,
emphasizing their practical motivation and mathematical
justification. Proofs of the main theorems are provided and data
examples illustrate practical aspects. This book will be a valuable
resource for researchers and graduate students in statistics,
mathematics, econometrics and other quantitative areas, as well as
for practitioners and applied researchers who need to analyze data
in which long memory, power laws, self-similar scaling or fractal
properties are relevant.
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