Dependence In Probability And Statistics
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Dependence In Probability And Statistics
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Author : Patrice Bertail
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-24
Dependence In Probability And Statistics written by Patrice Bertail and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-24 with Mathematics categories.
This book gives an account of recent developments in the field of probability and statistics for dependent data. It covers a wide range of topics from Markov chain theory and weak dependence with an emphasis on some recent developments on dynamical systems, to strong dependence in times series and random fields. There is a section on statistical estimation problems and specific applications. The book is written as a succession of papers by field specialists, alternating general surveys, mostly at a level accessible to graduate students in probability and statistics, and more general research papers mainly suitable to researchers in the field.
Dependence In Probability And Statistics
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Author : Paul Doukhan
language : en
Publisher: Springer Science & Business Media
Release Date : 2010-07-23
Dependence In Probability And Statistics written by Paul Doukhan and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-07-23 with Mathematics categories.
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.
Dependence In Probability And Statistics
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Author : Murad Taqqu
language : de
Publisher: Springer-Verlag
Release Date : 2019-06-12
Dependence In Probability And Statistics written by Murad Taqqu and has been published by Springer-Verlag this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-12 with Mathematics categories.
Statistical Learning For Big Dependent Data
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Author : Daniel Peña
language : en
Publisher: John Wiley & Sons
Release Date : 2021-05-04
Statistical Learning For Big Dependent Data written by Daniel Peña and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-04 with Mathematics categories.
Master advanced topics in the analysis of large, dynamically dependent datasets with this insightful resource Statistical Learning with Big Dependent Data delivers a comprehensive presentation of the statistical and machine learning methods useful for analyzing and forecasting large and dynamically dependent data sets. The book presents automatic procedures for modelling and forecasting large sets of time series data. Beginning with some visualization tools, the book discusses procedures and methods for finding outliers, clusters, and other types of heterogeneity in big dependent data. It then introduces various dimension reduction methods, including regularization and factor models such as regularized Lasso in the presence of dynamical dependence and dynamic factor models. The book also covers other forecasting procedures, including index models, partial least squares, boosting, and now-casting. It further presents machine-learning methods, including neural network, deep learning, classification and regression trees and random forests. Finally, procedures for modelling and forecasting spatio-temporal dependent data are also presented. Throughout the book, the advantages and disadvantages of the methods discussed are given. The book uses real-world examples to demonstrate applications, including use of many R packages. Finally, an R package associated with the book is available to assist readers in reproducing the analyses of examples and to facilitate real applications. Analysis of Big Dependent Data includes a wide variety of topics for modeling and understanding big dependent data, like: New ways to plot large sets of time series An automatic procedure to build univariate ARMA models for individual components of a large data set Powerful outlier detection procedures for large sets of related time series New methods for finding the number of clusters of time series and discrimination methods , including vector support machines, for time series Broad coverage of dynamic factor models including new representations and estimation methods for generalized dynamic factor models Discussion on the usefulness of lasso with time series and an evaluation of several machine learning procedure for forecasting large sets of time series Forecasting large sets of time series with exogenous variables, including discussions of index models, partial least squares, and boosting. Introduction of modern procedures for modeling and forecasting spatio-temporal data Perfect for PhD students and researchers in business, economics, engineering, and science: Statistical Learning with Big Dependent Data also belongs to the bookshelves of practitioners in these fields who hope to improve their understanding of statistical and machine learning methods for analyzing and forecasting big dependent data.
Stochastic Ordering And Dependence In Applied Probability
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Author : R. Szekli
language : en
Publisher: Springer
Release Date : 1995-01-24
Stochastic Ordering And Dependence In Applied Probability written by R. Szekli and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 1995-01-24 with Mathematics categories.
This book presents an introductory course in stochastic orderings and dependence and their applications to queues and networks of queues. Readers are assumed to have a firm grounding in Lebesgue measure, conditional expectation, and martingales. Chapter 1 presents a collection of one-dimensional orderings with applications to the theory of queues. Chapter 2 extends these concepts to stochastic orderings in many dimensional spaces and functional spaces. Then results are given on stochastic ordering of networks, replacement policies, and single-server queues associated with Markov renewal processes. Finally, Chapter 3 is devoted to dependence and the relations between dependence and orderings, and it includes applications to queueing networks and point processes.
Weak Dependence With Examples And Applications
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Author : Jérôme Dedecker
language : en
Publisher: Springer Science & Business Media
Release Date : 2007-07-18
Weak Dependence With Examples And Applications written by Jérôme Dedecker and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-18 with Mathematics categories.
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.
Decoupling
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Author : Victor de la Peña
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Decoupling written by Victor de la Peña and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
Decoupling theory provides a general framework for analyzing problems involving dependent random variables as if they were independent. It was born in the early eighties as a natural continuation of martingale theory and has acquired a life of its own due to vigorous development and wide applicability. The authors provide a friendly and systematic introduction to the theory and applications of decoupling. The book begins with a chapter on sums of independent random variables and vectors, with maximal inequalities and sharp estimates on moments which are later used to develop and interpret decoupling inequalities. Decoupling is first introduced as it applies in two specific areas, randomly stopped processes (boundary crossing problems) and unbiased estimation (U-- statistics and U--processes), where it has become a basic tool in obtaining several definitive results. In particular, decoupling is an essential component in the development of the asymptotic theory of U-- statistics and U--processes. The authors then proceed with the theory of decoupling in full generality. Special attention is given to comparison and interplay between martingale and decoupling theory, and to applications. Among other results, the applications include limit theorems, momemt and exponential inequalities for martingales and more general dependence structures, results with biostatistical implications, and moment convergence in Anscombe's theorem and Wald's equation for U--statistics. This book is addressed to researchers in probability and statistics and to graduate students. The expositon is at the level of a second graduate probability course, with a good portion of the material fit for use in a first year course. Victor de la Pe$a is Associate Professor of Statistics at Columbia University and is one of the more active developers of decoupling
Dependence In Probability And Statistics
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Author : Paul Doukhan
language : en
Publisher:
Release Date : 2010
Dependence In Probability And Statistics written by Paul Doukhan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Dependence (Statistics) categories.
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.
Direction Dependence In Statistical Modeling
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Author : Wolfgang Wiedermann
language : en
Publisher: John Wiley & Sons
Release Date : 2020-11-24
Direction Dependence In Statistical Modeling written by Wolfgang Wiedermann and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-24 with Mathematics categories.
Covers the latest developments in direction dependence research Direction Dependence in Statistical Modeling: Methods of Analysis incorporates the latest research for the statistical analysis of hypotheses that are compatible with the causal direction of dependence of variable relations. Having particular application in the fields of neuroscience, clinical psychology, developmental psychology, educational psychology, and epidemiology, direction dependence methods have attracted growing attention due to their potential to help decide which of two competing statistical models is more likely to reflect the correct causal flow. The book covers several topics in-depth, including: A demonstration of the importance of methods for the analysis of direction dependence hypotheses A presentation of the development of methods for direction dependence analysis together with recent novel, unpublished software implementations A review of methods of direction dependence following the copula-based tradition of Sungur and Kim A presentation of extensions of direction dependence methods to the domain of categorical data An overview of algorithms for causal structure learning The book's fourteen chapters include a discussion of the use of custom dialogs and macros in SPSS to make direction dependence analysis accessible to empirical researchers.
Empirical Process Techniques For Dependent Data
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Author : Herold Dehling
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Empirical Process Techniques For Dependent Data written by Herold Dehling and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Mathematics categories.
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,