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Weakly Dependent Stochastic Sequences And Their Applications Asymptotic Statistics Based On Weakly Dependent Data
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Book Synopsis Weakly Dependent Stochastic Sequences and Their Applications: Asymptotic statistics based on weakly dependent data by : Ken-ichi Yoshihara
Download or read book Weakly Dependent Stochastic Sequences and Their Applications: Asymptotic statistics based on weakly dependent data written by Ken-ichi Yoshihara and published by . This book was released on 1992 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Asymptotic Theory of Weakly Dependent Random Processes by : Emmanuel Rio
Download or read book Asymptotic Theory of Weakly Dependent Random Processes written by Emmanuel Rio and published by Springer. This book was released on 2017-04-13 with total page 211 pages. Available in PDF, EPUB and Kindle. Book excerpt: Ces notes sont consacrées aux inégalités et aux théorèmes limites classiques pour les suites de variables aléatoires absolument régulières ou fortement mélangeantes au sens de Rosenblatt. Le but poursuivi est de donner des outils techniques pour l'étude des processus faiblement dépendants aux statisticiens ou aux probabilistes travaillant sur ces processus.
Book Synopsis Weakly Dependent Stochastic Sequences and Their Applications: Statistical inference based on weakly dependent data by : Ken-ichi Yoshihara
Download or read book Weakly Dependent Stochastic Sequences and Their Applications: Statistical inference based on weakly dependent data written by Ken-ichi Yoshihara and published by . This book was released on 1992 with total page 408 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Weakly Dependent Stochastic Sequences and Their Applications: Order statistics based on weakly dependent data by : Ken-ichi Yoshihara
Download or read book Weakly Dependent Stochastic Sequences and Their Applications: Order statistics based on weakly dependent data written by Ken-ichi Yoshihara and published by . This book was released on 1992 with total page 360 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Weakly Dependent Stochastic Sequences and Their Applications: Curve estimation based on weakly dependent data by : Ken-ichi Yoshihara
Download or read book Weakly Dependent Stochastic Sequences and Their Applications: Curve estimation based on weakly dependent data written by Ken-ichi Yoshihara and published by . This book was released on 1992 with total page 356 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Weakly Dependent Stochastic Sequences and Their Applications: Generalized partial-sum processes by : Ken-ichi Yoshihara
Download or read book Weakly Dependent Stochastic Sequences and Their Applications: Generalized partial-sum processes written by Ken-ichi Yoshihara and published by . This book was released on 1996 with total page 410 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Weakly Dependent Stochastic Sequences and Their Applications by : Ken-ichi Yoshihara
Download or read book Weakly Dependent Stochastic Sequences and Their Applications written by Ken-ichi Yoshihara and published by . This book was released on 1994 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonparametric Functional Data Analysis by : Frédéric Ferraty
Download or read book Nonparametric Functional Data Analysis written by Frédéric Ferraty and published by Springer Science & Business Media. This book was released on 2006-11-22 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern apparatuses allow us to collect samples of functional data, mainly curves but also images. On the other hand, nonparametric statistics produces useful tools for standard data exploration. This book links these two fields of modern statistics by explaining how functional data can be studied through parameter-free statistical ideas. At the same time it shows how functional data can be studied through parameter-free statistical ideas, and offers an original presentation of new nonparametric statistical methods for functional data analysis.
Book Synopsis Weak Dependence: With Examples and Applications by : Jérome Dedecker
Download or read book Weak Dependence: With Examples and Applications written by Jérome Dedecker and published by Springer Science & Business Media. This book was released on 2007-07-29 with total page 326 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book develops Doukhan/Louhichi's 1999 idea to measure asymptotic independence of a random process. The authors, who helped develop this theory, propose examples of models fitting such conditions: stable Markov chains, dynamical systems or more complicated models, nonlinear, non-Markovian, and heteroskedastic models with infinite memory. Applications are still needed to develop a method of analysis for nonlinear times series, and this book provides a strong basis for additional studies.
Book Synopsis Modeling Uncertainty by : Moshe Dror
Download or read book Modeling Uncertainty written by Moshe Dror and published by Springer. This book was released on 2019-11-05 with total page 782 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modeling Uncertainty: An Examination of Stochastic Theory, Methods, and Applications, is a volume undertaken by the friends and colleagues of Sid Yakowitz in his honor. Fifty internationally known scholars have collectively contributed 30 papers on modeling uncertainty to this volume. Each of these papers was carefully reviewed and in the majority of cases the original submission was revised before being accepted for publication in the book. The papers cover a great variety of topics in probability, statistics, economics, stochastic optimization, control theory, regression analysis, simulation, stochastic programming, Markov decision process, application in the HIV context, and others. There are papers with a theoretical emphasis and others that focus on applications. A number of papers survey the work in a particular area and in a few papers the authors present their personal view of a topic. It is a book with a considerable number of expository articles, which are accessible to a nonexpert - a graduate student in mathematics, statistics, engineering, and economics departments, or just anyone with some mathematical background who is interested in a preliminary exposition of a particular topic. Many of the papers present the state of the art of a specific area or represent original contributions which advance the present state of knowledge. In sum, it is a book of considerable interest to a broad range of academic researchers and students of stochastic systems.
Book Synopsis Dependence in Probability and Statistics by : Paul Doukhan
Download or read book Dependence in Probability and Statistics written by Paul Doukhan and published by Springer Science & Business Media. This book was released on 2010-07-23 with total page 222 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.
Book Synopsis The Yokohama Mathematical Journal by :
Download or read book The Yokohama Mathematical Journal written by and published by . This book was released on 1994 with total page 342 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Bulletin of the Faculty of Engineering, Yokohama National University by :
Download or read book Bulletin of the Faculty of Engineering, Yokohama National University written by and published by . This book was released on 1996 with total page 578 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Empirical Process Techniques for Dependent Data by : Herold Dehling
Download or read book Empirical Process Techniques for Dependent Data written by Herold Dehling and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 378 pages. Available in PDF, EPUB and Kindle. Book excerpt: Empirical process techniques for independent data have been used for many years in statistics and probability theory. These techniques have proved very useful for studying asymptotic properties of parametric as well as non-parametric statistical procedures. Recently, the need to model the dependence structure in data sets from many different subject areas such as finance, insurance, and telecommunications has led to new developments concerning the empirical distribution function and the empirical process for dependent, mostly stationary sequences. This work gives an introduction to this new theory of empirical process techniques, which has so far been scattered in the statistical and probabilistic literature, and surveys the most recent developments in various related fields. Key features: A thorough and comprehensive introduction to the existing theory of empirical process techniques for dependent data * Accessible surveys by leading experts of the most recent developments in various related fields * Examines empirical process techniques for dependent data, useful for studying parametric and non-parametric statistical procedures * Comprehensive bibliographies * An overview of applications in various fields related to empirical processes: e.g., spectral analysis of time-series, the bootstrap for stationary sequences, extreme value theory, and the empirical process for mixing dependent observations, including the case of strong dependence. To date this book is the only comprehensive treatment of the topic in book literature. It is an ideal introductory text that will serve as a reference or resource for classroom use in the areas of statistics, time-series analysis, extreme value theory, point process theory, and applied probability theory. Contributors: P. Ango Nze, M.A. Arcones, I. Berkes, R. Dahlhaus, J. Dedecker, H.G. Dehling,
Book Synopsis Statistical Inference for Discrete Time Stochastic Processes by : M. B. Rajarshi
Download or read book Statistical Inference for Discrete Time Stochastic Processes written by M. B. Rajarshi and published by Springer Science & Business Media. This book was released on 2014-07-08 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work is an overview of statistical inference in stationary, discrete time stochastic processes. Results in the last fifteen years, particularly on non-Gaussian sequences and semi-parametric and non-parametric analysis have been reviewed. The first chapter gives a background of results on martingales and strong mixing sequences, which enable us to generate various classes of CAN estimators in the case of dependent observations. Topics discussed include inference in Markov chains and extension of Markov chains such as Raftery's Mixture Transition Density model and Hidden Markov chains and extensions of ARMA models with a Binomial, Poisson, Geometric, Exponential, Gamma, Weibull, Lognormal, Inverse Gaussian and Cauchy as stationary distributions. It further discusses applications of semi-parametric methods of estimation such as conditional least squares and estimating functions in stochastic models. Construction of confidence intervals based on estimating functions is discussed in some detail. Kernel based estimation of joint density and conditional expectation are also discussed. Bootstrap and other resampling procedures for dependent sequences such as Markov chains, Markov sequences, linear auto-regressive moving average sequences, block based bootstrap for stationary sequences and other block based procedures are also discussed in some detail. This work can be useful for researchers interested in knowing developments in inference in discrete time stochastic processes. It can be used as a material for advanced level research students.
Book Synopsis Data Analysis and Related Applications 4 by : Yiannis Dimotikalis
Download or read book Data Analysis and Related Applications 4 written by Yiannis Dimotikalis and published by John Wiley & Sons. This book was released on 2024-10-08 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Introduction to Empirical Processes and Semiparametric Inference by : Michael R. Kosorok
Download or read book Introduction to Empirical Processes and Semiparametric Inference written by Michael R. Kosorok and published by Springer Science & Business Media. This book was released on 2007-12-29 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: Kosorok’s brilliant text provides a self-contained introduction to empirical processes and semiparametric inference. These powerful research techniques are surprisingly useful for developing methods of statistical inference for complex models and in understanding the properties of such methods. This is an authoritative text that covers all the bases, and also a friendly and gradual introduction to the area. The book can be used as research reference and textbook.
