Statistics for Long-Memory Processes
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A01=Jan Beran
Approximate Log Likelihood Function
Approximate MLE
Asymptotic Coverage Probability
Author_Jan Beran
Category=PBT
Cumulative Periodograms
Data Set
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
fractional
Fractional ARIMA
Fractional ARIMA Model
Fractional ARIMA Process
Fractional Gaussian Noise
frequency domain methods
gaussian
Hermite Rank
hydrological modeling
Independent Standard Exponential Random Variables
Independent Standard Normal Random Variables
Jan Beran
Log Log Coordinates
Long Range Dependence
long-range dependence data analysis
NBS Data
Nile River Data
noise
Ordinary Brownian Motion
Periodogram Ordinates
Regression Spectrum
robust parameter estimation
Rosenblatt Process
S-PLUS statistical programming
Self-similar Processes
Self-similarity Parameter
Short Memory Processes
stochastic processes
time series analysis
Typical Sample Paths
VBR
Product details
- ISBN 9780412049019
- Weight: 1080g
- Dimensions: 138 x 216mm
- Publication Date: 01 Oct 1994
- Publisher: Taylor & Francis Ltd
- Publication City/Country: GB
- Product Form: Hardback
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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.
