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Condition: good. The item shows wear from consistent use, but it remains in good condition and works perfectly. All pages and cover are intact including the dust cover, if applicable . Spine may show signs of wear. Pages may include limited notes and highlighting. May NOT include discs, access code or other supplemental materials.
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Dependence in Probability and Statistics
Book 33 of 72: Lecture Notes in StatisticsSeller: Hackenberg Booksellers ABAA, El Cerrito, CA, U.S.A.
Seller rating 5 out of 5 stars
viii, 492p., stiff library boards, ex libris (Lecture notes in statistics, 187).
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Dependence in Probability and Statistics (Lecture Notes in Statistics, 187)
Book 33 of 72: Lecture Notes in StatisticsLanguage: English
Published by Springer, New York, NY, 2006
ISBN 10: 0387317414 ISBN 13: 9780387317410
Seller: Book Bear, West Brookfield, MA, U.S.A.
Association Member: SNEAB
Seller rating 5 out of 5 stars
Paperback. Condition: Very Good. 489 pp. Tightly bound. Spine not compromised. Text is free of markings. No ownership markings. NOTE: The word "USED" is neatly stamped on the top fore-edge. Lecture Notes In Statistics 187.
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US$ 28.90
US$ 21.73 shipping
Ships from United Kingdom to U.S.A.Quantity: 1 available
Add to basketCondition: Good. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. Library sticker on front cover. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1550grams, ISBN:9780817641689.
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Dependence in Probability and Statistics (Lecture Notes in Statistics, 187)
Book 33 of 72: Lecture Notes in StatisticsLanguage: English
Published by Springer, New York, NY, 2006
ISBN 10: 0387317414 ISBN 13: 9780387317410
First Edition
Paperback. Condition: Very Good. 1st Edition. 492 pp. Tightly bound. Spine not compromised. Text is free of markings. No ownership markings. NOTE: The word "USED" neatly stamped on the top fore-edge. First Edition / First Printing. 9,8,7,6,5,4,3,2,1.
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Dependence in Probability and Statistics (Lecture Notes in Statistics, 187)
Book 33 of 72: Lecture Notes in StatisticsLanguage: English
Published by Springer, New York, NY, 2006
ISBN 10: 0387317414 ISBN 13: 9780387317410
Paperback. Condition: Very Good. 489 pp. Tightly bound. Spine not compromised. Text is free of markings. NOTE: The word "USED" is neatly stamped on the top fore-edge. Lecture Notes In Statistics 187.
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Dependence in Probability and Statistics (Paperback)
Book 46 of 72: Lecture Notes in StatisticsLanguage: English
Published by Springer-Verlag Berlin and Heidelberg GmbH & Co. KG, Berlin, 2010
ISBN 10: 364214103X ISBN 13: 9783642141034
Paperback. Condition: new. Paperback. This volume contains several contributions on the general theme of dependence for several classes of stochastic processes, andits implicationson asymptoticproperties of various statistics and on statistical inference issues in statistics and econometrics. The chapter by Berkes, Horvath and Schauer is a survey on their recent results on bootstrap and permutation statistics when the negligibility condition of classical central limit theory is not satis ed. These results are of interest for describing the asymptotic properties of bootstrap and permutation statistics in case of in nite va- ances, and for applications to statistical inference, e.g., the change-point problem. The paper by Stoev reviews some recent results by the author on ergodicity of max-stable processes. Max-stable processes play a central role in the modeling of extreme value phenomena and appear as limits of component-wise maxima. At the presenttime,arathercompleteandinterestingpictureofthedependencestructureof max-stable processes has emerged,involvingspectral functions, extremalstochastic integrals, mixed moving maxima, and other analytic and probabilistic tools.For statistical applications, the problem of ergodicity or non-ergodicity is of primary importance. This account of recent works on weakly dependent, long memory and multifractal processes introduces new dependence measures for studying complex stochastic systems and includes other topics such as the dependence structure of max-stable processes. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condition: As New. Unread book in perfect condition.
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Softcover. Condition: As New. 1. Auflage. unread, like new.
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Dependence in Probability and Statistics (Lecture Notes in Statistics, 200)
Book 46 of 72: Lecture Notes in StatisticsSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 69.39
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US$ 67.06
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Condition: New. pp. 228.
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Dependence in Probability and Statistics
Book 46 of 72: Lecture Notes in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 70.55
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Paperback or Softback. Condition: New. Dependence in Probability and Statistics. Book.
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Dependence in Probability and Statistics
Book 46 of 72: Lecture Notes in StatisticsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 78.28
US$ 20.50 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: As New. Unread book in perfect condition.
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Hardcover. Condition: Good. Oversized & heavy hardcover, x + 719 pages. Stamped "withdrawn". A thick black marker line on upper page edges externally; a couple of blank stickers inside the front board; neatly removed title page. Else book shows little wear, interior is clean and bright with unmarked text, firmly bound. Issued without a dust jacket. -- Contents: Preface; Part A: Theory I. Probability -- Fractional Brownian Motion and Long-Range Dependence; Historical Comments Related to Fractional Brownian Motion; Models, Inequalities and Limit Theorems for Stationary Sequences; Limit Theorems Under Seasonal Long-Memory; CLTs for Polynomials of Linear Sequences: Diagram Formula with Illustrations; Non-CLTs: U-Statistics, Multinomial Formula and Approximations of Multiple Ito-Wiener Integrals; A Decomposition for Generalized U-Statistics of Long-Memory Linear Processes; Limit Theorems for Infinite Variance Sequences; Fractional Calculus and Its Connection to Fractional Brownian Motion; Stochastic Integration, Filtering with Respect to Fractional Brownian Motion; II. Statistics -- Parametric Estimation Under Long-Range Dependence; Semiparametric Spectral Estimation for Fractional Processes; Nonparametric Estimation for Long-Range Dependent Sequences; Estimation of Long Memory in Volatility; Detection and Estimation of Changes in Regime; Robust Estimators in Regression Models with Long Memory Errors; Prediction of Long-Memory Time Series; Part B: Applications III. Applications -- Long-Range Dependence and Data Network Traffic; Large Deviations of Queues with Long-Range Dependent Input; Long-Range Dependence Paradigm for Macroeconomics and Finance; Long-Range Dependence Effects and ARCH Modeling; Long-Range Dependence in Hydrology; Wavelet Based Estimation of Local Kolmogorov Turbulence; Limit Theorems for the Burgers Equation Initialized by Data with Long-Range Dependence; IV. Methodology -- Self-Similarity and Long-Range Dependence Through the Wavelet Lens; Semi-Parametric Estimation of the Long-Range Dependence Parameter: A Survey; Generators of Long-Range Dependent Processes: A Survey; Multifractal Processes -- The area of data analysis has been greatly affected by our computer age. For example, the issue of collecting and storing huge data sets has become quite simplified and has greatly affected such areas as finance and telecommunications. Even non-specialists try to analyze data sets and ask basic questions about their structure. One such question is whether one observes some type of invariance with respect to scale, a question that is closely related to the existence of long-range dependence in the data. This important topic of long-range dependence is the focus of this unique work, written by a number of specialists on the subject. The topics selected should give a good overview from the probabilistic and statistical perspective. Included will be articles on fractional Brownian motion, models, inequalities and limit theorems, periodic long-range dependence, parametric, semiparametric, and non-parametric estimation, long-memory stochastic volatility models, robust estimation, and prediction for long-range dependence sequences. For those graduate students and researchers who want to use the methodology and need to know the "tricks of the trade," there will be a special section called "Mathematical Techniques." Topics in the first part of the book are covered from probabilistic and statistical perspectives and include fractional Brownian motion, models, inequalities and limit theorems, periodic long-range dependence, parametric, semiparametric, and non-parametric estimation, long-memory stochastic volatility models, robust estimation, prediction for long-range dependence sequences. The reader is referred to more detailed proofs if already found in the literature.
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Condition: New.
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Condition: New.
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Stochastic Models for Time Series (Mathématiques et Applications, 80)
Book 7 of 9: Mathématiques et ApplicationsSeller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
US$ 111.61
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Stochastic Models for Time Series (Paperback)
Book 7 of 9: Mathématiques et ApplicationsLanguage: English
Published by Springer International Publishing AG, Cham, 2018
ISBN 10: 3319769375 ISBN 13: 9783319769370
Paperback. Condition: new. Paperback. This book presents essential tools for modelling non-linear time series. The first part of the book describes the main standard tools of probability and statistics that directly apply to the time series context to obtain a wide range of modelling possibilities. Functional estimation and bootstrap are discussed, and stationarity is reviewed. The second part describes a number of tools from Gaussian chaos and proposes a tour of linear time series models. It goes on to address nonlinearity from polynomial or chaotic models for which explicit expansions are available, then turns to Markov and non-Markov linear models and discusses Bernoulli shifts time series models. Finally, the volume focuses on the limit theory, starting with the ergodic theorem, which is seen as the first step for statistics of time series. It defines the distributional range to obtain generic tools for limit theory under long or short-range dependences (LRD/SRD) and explains examples of LRD behaviours. More general techniques (central limit theorems) are described under SRD; mixing and weak dependence are also reviewed. In closing, it describes moment techniques together with their relations to cumulant sums as well as an application to kernel type estimation.The appendix reviews basic probability theory facts and discusses useful laws stemming from the Gaussian laws as well as the basic principles of probability, and is completed by R-scripts used for the figures. Richly illustrated with examples and simulations, the book is recommended for advanced master courses for mathematicians just entering the field of time series, and statisticians who want more mathematical insights into the background of non-linear time series. This book presents essential tools for modelling non-linear time series. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
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Condition: As New. Unread book in perfect condition.
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Stochastic Models for Time Series
Book 7 of 9: Mathématiques et ApplicationsSeller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
US$ 107.51
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US$ 111.44
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Add to basketPF. Condition: New.
