Описание: In this practical and comprehensive guide, author Martin Kleppmann helps you navigate this diverse landscape by examining the pros and cons of various technologies for processing and storing data.
Описание: Data analysis as an area of importance has grown exponentially, especially during the past couple of decades. This can be attributed to a rapidly growing computer industry and the wide applicability of computational techniques, in conjunction with new advances of analytic tools. This being the case, the need for literature that addresses this is self-evident. New publications are appearing, covering the need for information from all fields of science and engineering, thanks to the universal relevance of data analysis and statistics packages. This book is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians who have been working at the forefront of data analysis. The chapters included in this volume represent a cross-section of current concerns and research interests in these scientific areas. The material is divided into two parts: Computational Data Analysis, and Classification Data Analysis, with methods for both - providing the reader with both theoretical and applied information on data analysis methods, models and techniques and appropriate applications.
Описание: An examination of system reliability theory, this title features in-depth discussion of dependability management and reliability centered systems as well as functional safety issues-matters critical to the IEC standards.
Описание: Our original objective in writing this book was to demonstrate how the concept of the equation of motion of a Brownian particle — the Langevin equation or Newtonian-like evolution equation of the random phase space variables describing the motion — first formulated by Langevin in 1908 — so making him inter alia the founder of the subject of stochastic differential equations, may be extended to solve the nonlinear problems arising from the Brownian motion in a potential. Such problems appear under various guises in many diverse applications in physics, chemistry, biology, electrical engineering, etc. However, they have been invariably treated (following the original approach of Einstein and Smoluchowski) via the Fokker-Planck equation for the evolution of the probability density function in phase space. Thus the more simple direct dynamical approach of Langevin which we use and extend here, has been virtually ignored as far as the Brownian motion in a potential is concerned. In addition two other considerations have driven us to write this new edition of The Langevin Equation. First, more than five years have elapsed since the publication of the third edition and following many suggestions and comments of our colleagues and other interested readers, it became increasingly evident to us that the book should be revised in order to give a better presentation of the contents. In particular, several chapters appearing in the third edition have been rewritten so as to provide a more direct appeal to the particular community involved and at the same time to emphasize via a synergetic approach how seemingly unrelated physical problems all involving random noise may be described using virtually identical mathematical methods. Secondly, in that period many new and exciting developments have occurred in the application of the Langevin equation to Brownian motion. Consequently, in order to accommodate all these, a very large amount of new material has been added so as to present a comprehensive overview of the subject.
Описание: The purpose of this book is to present results on the subject of weak convergence in function spaces to study invariance principles in statistical applications to dependent random variables, U-statistics, censor data analysis. Different techniques, formerly available only in a broad range of literature, are for the first time presented here in a self-contained fashion. Contents:Weak convergence of stochastic processesWeak convergence in metric spacesWeak convergence on C[0, 1] and D[0,?)Central limit theorem for semi-martingales and applicationsCentral limit theorems for dependent random variablesEmpirical processBibliography
Описание: This book introduces data-driven remaining useful life prognosis techniques, and shows how to utilize the condition monitoring data to predict the remaining useful life of stochastic degrading systems and to schedule maintenance and logistics plans.
BIG DATA, ARTIFICIAL INTELLIGENCE AND DATA ANALYSIS SET Coordinated by Jacques Janssen
Data analysis is a scientific field that continues to grow enormously, most notably over the last few decades, following rapid growth within the tech industry, as well as the wide applicability of computational techniques alongside new advances in analytic tools. Modeling enables data analysts to identify relationships, make predictions, and to understand, interpret and visualize the extracted information more strategically.
This book includes the most recent advances on this topic, meeting increasing demand from wide circles of the scientific community. Applied Modeling Techniques and Data Analysis 2 is a collective work by a number of leading scientists, analysts, engineers, mathematicians and statisticians, working on the front end of data analysis and modeling applications. The chapters cover a cross section of current concerns and research interests in the above scientific areas. The collected material is divided into appropriate sections to provide the reader with both theoretical and applied information on data analysis methods, models and techniques, along with appropriate applications.
Автор: Little Название: Statistical Analysis with Missing Data, Third Edit ion ISBN: 0470526793 ISBN-13(EAN): 9780470526798 Издательство: Wiley Рейтинг: Цена: 12664.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Reflecting new application topics, Statistical Analysis with Missing Data offers a thoroughly up-to-date, reorganized survey of current methodology for handling missing data problems. The third edition reviews historical approaches to the subject and describe rigorous yet simple methods for multivariate analysis with missing values.
Provides a comprehensive and updated study of GARCH models and their applications in finance, covering new developments in the discipline
This book provides a comprehensive and systematic approach to understanding GARCH time series models and their applications whilst presenting the most advanced results concerning the theory and practical aspects of GARCH. The probability structure of standard GARCH models is studied in detail as well as statistical inference such as identification, estimation, and tests. The book also provides new coverage of several extensions such as multivariate models, looks at financial applications, and explores the very validation of the models used.
GARCH Models: Structure, Statistical Inference and Financial Applications, 2nd Edition features a new chapter on Parameter-Driven Volatility Models, which covers Stochastic Volatility Models and Markov Switching Volatility Models. A second new chapter titled Alternative Models for the Conditional Variance contains a section on Stochastic Recurrence Equations and additional material on EGARCH, Log-GARCH, GAS, MIDAS, and intraday volatility models, among others. The book is also updated with a more complete discussion of multivariate GARCH; a new section on Cholesky GARCH; a larger emphasis on the inference of multivariate GARCH models; a new set of corrected problems available online; and an up-to-date list of references.
Features up-to-date coverage of the current research in the probability, statistics, and econometric theory of GARCH models
Covers significant developments in the field, especially in multivariate models
Contains completely renewed chapters with new topics and results
Handles both theoretical and applied aspects
Applies to researchers in different fields (time series, econometrics, finance)
Includes numerous illustrations and applications to real financial series
Presents a large collection of exercises with corrections
Supplemented by a supporting website featuring R codes, Fortran programs, data sets and Problems with corrections
GARCH Models, 2nd Edition is an authoritative, state-of-the-art reference that is ideal for graduate students, researchers, and practitioners in business and finance seeking to broaden their skills of understanding of econometric time series models.
Автор: Bandyopadhyay S et al Название: Models for Social Networks With Statistical Applications ISBN: 1412941687 ISBN-13(EAN): 9781412941686 Издательство: Sage Publications Рейтинг: Цена: 18058.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: Written by a sociologist, a graph theorist, and a statistician, this title provides you with a solid statistical foundation from which to analyse network data.
Описание: Serving as a text for a two semester sequence on probability and statistical inference complex Models for Probability and Statistical Inference: Theory and Applications features exercises throughout the book and selected answers (not solutions). Each section is followed by a selection of problems, from simple to more complex.
Since the publication of the Second Edition of this popular textbook, new standards have changed the focus of reliability engineering, which introduced new concepts and terminology. Consequently, the Third Edition of System Reliability Theory: Models, Statistical Methods, and Applications has been thoroughly rewritten and updated to meet current standards. With an updated practical focus, incorporation of industry feedback, and many new examples based on real-world industry problems and data, this book begins with an introduction on reliability engineering and is followed by coverage on failures and failure analysis. The authors address failure models and qualitative system analysis and present new coverage on state space models. In addition, a new chapter on component reliability and availability is followed by a chapter on systems of independent components. Component importance is covered followed by a chapter on dependent failures, which now includes a discussion on causes of common cause failures, explicit versus implicit modeling, and the Beta-factor model. The authors also discuss counting processes and Markov Processes. In addition, the authors provide new sections on: maintenance assessment and optimization; advanced models failure rates; human errors; software bugs; CCFs (ICED + method in IEC 61508); generic failure rate databases; FRACAS data; application-specific data; frequency of dangerous failures (PFH); and reliability prediction. The book is supplemented with a companion website, which contains an Instructor Solutions Manual, lecture slides, reliability data sources, sample exam questions, and a terminology review.
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