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Record Nr. |
UNINA9910739406403321 |
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Titolo |
Long-Memory Processes : Probabilistic Properties and Statistical Methods / / by Jan Beran, Yuanhua Feng, Sucharita Ghosh, Rafal Kulik |
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Pubbl/distr/stampa |
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Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2013 |
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ISBN |
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Edizione |
[1st ed. 2013.] |
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Descrizione fisica |
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1 online resource (892 p.) |
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Altri autori (Persone) |
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Disciplina |
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Soggetti |
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Statistics |
Probabilities |
Biometry |
Statistical Theory and Methods |
Probability Theory |
Statistics in Business, Management, Economics, Finance, Insurance |
Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences |
Biostatistics |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Note generali |
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Description based upon print version of record. |
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Nota di bibliografia |
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Includes bibliographical references and indexes. |
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Nota di contenuto |
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Definition of Long Memory -- Origins and Generation of Long Memory -- Mathematical Concepts -- Limit Theorems -- Statistical Inference for Stationary Processes -- Statistical Inference for Nonlinear Processes -- Statistical Inference for Nonstationary Processes -- Forecasting -- Spatial and Space-Time Processes -- Resampling -- Function Spaces -- Regularly Varying Functions -- Vague Convergence -- Some Useful Integrals -- Notation and Abbreviations. |
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Sommario/riassunto |
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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 |
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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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