Doukhan paul (78 résultats)

Etat : good. A copy that has been read, remains in good condition. All pages are intact, and the cover is intact. The spine and cover show signs of wear. Pages can include notes and highlighting and show signs of wear, and the copy can include "From the library of" labels or previous owner inscriptions. 100% GUARANTEE! Shipped with delivery confirmation, if you're not satisfied with purchase please return item! Ships via media mail.…

Etat : Very Good. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Paperback. Etat : 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.

Paperback. Etat : 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.

Etat : Very Good. 2006th Edition. Former library copy. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Includes library markings. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good. …

Etat : New.

Paperback or Softback. Etat : New. Dependence in Probability and Statistics. Book.

Etat : As New. Unread book in perfect condition.

Etat : New. In English.

Softcover. Etat : As New. 1. Auflage. unread, like new.

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Etat : New. pp. 228.

Hardcover. Etat : 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 Stamped "withdrawn".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.…

Etat : New.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - This volume collects recent works on weakly dependent, long-memory and multifractal processes and introduces new dependence measures for studying complex stochastic systems. Other topics include the statistical theory for bootstrap and permutation statistics for infinite variance processes, the dependence structure of max-stable processes, and the statistical properties of spectral estimators of the long memory parameter. The asymptotic behavior of Fejér graph integrals and their use for proving central limit theorems for tapered estimators are investigated. New multifractal processes are introduced and their multifractal properties analyzed. Wavelet-based methods are used to study multifractal processes with different multiresolution quantities, and to detect changes in the variance of random processes. Linear regression models with long-range dependent errors are studied, as is the issue of detecting changes in their parameters.…

Etat : As New. Unread book in perfect condition.

Etat : New.

PF. Etat : New.

Etat : New.

Etat : New. In English.

Taschenbuch. Etat : Neu. Dependence in Probability and Statistics | Paul Doukhan (u. a.) | Taschenbuch | xv | Englisch | 2010 | Springer | EAN 9783642141034 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Etat : As New. Unread book in perfect condition.

Etat : New.

Etat : New.

Taschenbuch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - 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 proposesa 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.…

paperback. Etat : New. In shrink wrap. Looks like an interesting title.

Etat : New.

Etat : New. In English.