Parametric Statistical Change Point Analysis

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Parametric Statistical Change Point Analysis

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Author : Jie Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2011-11-06

Parametric Statistical Change Point Analysis written by Jie Chen 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 2011-11-06 with Mathematics categories.

This revised and expanded second edition is an in-depth study of the change point problem from a general point of view, as well as a further examination of change point analysis of the most commonly used statistical models. Change point problems are encountered in such disciplines as economics, finance, medicine, psychology, signal processing, and geology, to mention only several. More recently, change point analysis has been found in extensive applications related to analyzing biomedical imaging data and gene expression data. Extensive examples throughout the text emphasize key concepts and different methodologies used. New examples of change point analysis in modern molecular biology and other fields such as finance and air traffic control have been added to this second edition.

Parametric Statistical Change Point Analysis

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Author : Jie Chen
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-11-11

Parametric Statistical Change Point Analysis written by Jie Chen 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 2013-11-11 with Mathematics categories.

Recently there has been a keen interest in the statistical analysis of change point detec tion and estimation. Mainly, it is because change point problems can be encountered in many disciplines such as economics, finance, medicine, psychology, geology, litera ture, etc. , and even in our daily lives. From the statistical point of view, a change point is a place or time point such that the observations follow one distribution up to that point and follow another distribution after that point. Multiple change points problem can also be defined similarly. So the change point(s) problem is two fold: one is to de cide if there is any change (often viewed as a hypothesis testing problem), another is to locate the change point when there is a change present (often viewed as an estimation problem). The earliest change point study can be traced back to the 1950s. During the fol lowing period of some forty years, numerous articles have been published in various journals and proceedings. Many of them cover the topic of single change point in the means of a sequence of independently normally distributed random variables. Another popularly covered topic is a change point in regression models such as linear regres sion and autoregression. The methods used are mainly likelihood ratio, nonparametric, and Bayesian. Few authors also considered the change point problem in other model settings such as the gamma and exponential.

Nonparametric Methods In Change Point Problems

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Author : E. Brodsky
language : en
Publisher: Springer Science & Business Media
Release Date : 1993-01-31

Nonparametric Methods In Change Point Problems written by E. Brodsky 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 1993-01-31 with Mathematics categories.

The explosive development of information science and technology puts in new problems involving statistical data analysis. These problems result from higher re quirements concerning the reliability of statistical decisions, the accuracy of math ematical models and the quality of control in complex systems. A new aspect of statistical analysis has emerged, closely connected with one of the basic questions of cynergetics: how to "compress" large volumes of experimental data in order to extract the most valuable information from data observed. De tection of large "homogeneous" segments of data enables one to identify "hidden" regularities in an object's behavior, to create mathematical models for each seg ment of homogeneity, to choose an appropriate control, etc. Statistical methods dealing with the detection of changes in the characteristics of random processes can be of great use in all these problems. These methods have accompanied the rapid growth in data beginning from the middle of our century. According to a tradition of more than thirty years, we call this sphere of statistical analysis the "theory of change-point detection. " During the last fifteen years, we have witnessed many exciting developments in the theory of change-point detection. New promising directions of research have emerged, and traditional trends have flourished anew. Despite this, most of the results are widely scattered in the literature and few monographs exist. A real need has arisen for up-to-date books which present an account of important current research trends, one of which is the theory of non parametric change--point detection.

Bayesian Time Series Models

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Author : David Barber
language : en
Publisher: Cambridge University Press
Release Date : 2011-08-11

Bayesian Time Series Models written by David Barber and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-08-11 with Computers categories.

The first unified treatment of time series modelling techniques spanning machine learning, statistics, engineering and computer science.

Parametric Statistical Change Point Analysis

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Author : Jie Chen
language : en
Publisher: Birkhäuser
Release Date : 2000-06-15

Parametric Statistical Change Point Analysis written by Jie Chen and has been published by Birkhäuser this book supported file pdf, txt, epub, kindle and other format this book has been release on 2000-06-15 with Mathematics categories.

Recently there has been a keen interest in the statistical analysis of change point detec tion and estimation. Mainly, it is because change point problems can be encountered in many disciplines such as economics, finance, medicine, psychology, geology, litera ture, etc. , and even in our daily lives. From the statistical point of view, a change point is a place or time point such that the observations follow one distribution up to that point and follow another distribution after that point. Multiple change points problem can also be defined similarly. So the change point(s) problem is two fold: one is to de cide if there is any change (often viewed as a hypothesis testing problem), another is to locate the change point when there is a change present (often viewed as an estimation problem). The earliest change point study can be traced back to the 1950s. During the fol lowing period of some forty years, numerous articles have been published in various journals and proceedings. Many of them cover the topic of single change point in the means of a sequence of independently normally distributed random variables. Another popularly covered topic is a change point in regression models such as linear regres sion and autoregression. The methods used are mainly likelihood ratio, nonparametric, and Bayesian. Few authors also considered the change point problem in other model settings such as the gamma and exponential.

Change Point Analysis

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Author : Baisuo Jin
language : en
Publisher: CRC Press
Release Date : 2025-08-29

Change Point Analysis written by Baisuo Jin and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-08-29 with Mathematics categories.

Change point analysis is a crucial statistical technique for detecting structural breaks within datasets, applicable in diverse fields such as finance and weather forecasting. The authors of this book aim to consolidate recent advancements and broaden the scope beyond traditional time series applications to include biostatistics, longitudinal data analysis, high-dimensional data, and network analysis. The book introduces foundational concepts with practical data examples from literature, alongside discussions of related machine learning topics. Subsequent chapters focus on mathematical tools for single- and multiple-change point detection along with statistical inference issues, which provide rigorous proofs to enhance understanding but assume readers have foundational knowledge in graduate-level probability and statistics. The book also expands the discussion into threshold regression frameworks linked to subgroup identification in modern statistical learning and apply change point analysis to functional data and dynamic networks—areas not comprehensively covered elsewhere. Key Features: Comprehensive Coverage of Diverse Applications: This book expands the scope of change point analysis to include biostatistics, longitudinal data, high-dimensional data, and network analysis. This broad applicability makes it a valuable resource for researchers and students across various disciplines Integration of Theory and Practice: The book balances rigorous mathematical theory with practical applications by providing extensive computational examples using R. Each chapter features real-world data illustrations and discussions of relevant machine learning topics, ensuring that readers can see the relevance of theoretical concepts in applied settings Accessibility for Students: The content is designed with graduate-level students in mind, providing clear explanations and structured guidance through complex mathematical tools. Rigorous proofs are included to facilitate understanding without overwhelming readers with overly advanced theories early on The book incorporates computational results using R, showcasing various packages tailored for specific methods or problem domains while providing references for further exploration. By offering a selection of widely adopted methodologies relevant in scientific research as well as business contexts, this text aims to equip junior researchers with essential tools needed for their work in change point analysis.

Non Parametric Statistical Diagnosis

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Author : E. Brodsky
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-14

Non Parametric Statistical Diagnosis written by E. Brodsky 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 2013-03-14 with Mathematics categories.

This book has a distinct philosophy and it is appropriate to make it explicit at the outset. In our view almost all classic statistical inference is based upon the assumption (explicit or implicit) that there exists a fixed probabilistic mechanism of data generation. Unlike classic statistical inference, this book is devoted to the statistical analysis of data about complex objects with more than one probabilistic mechanism of data generation. We think that the exis tence of more than one data generation process (DGP) is the most important characteristic of com plex systems. When the hypothesis of statistical homogeneity holds true, Le., there exists only one mechanism of data generation, all statistical inference is based upon the fundamentallaws of large numbers. However, the situation is completely different when the probabilistic law of data generation can change (in time or in the phase space). In this case all data obtained must be 'sorted' in subsamples generated by different probabilistic mechanisms. Only after such classification we can make correct inferences about all DGPs. There exists yet another type of problem for complex systems. Here it is important to detect possible (but unpredictable) changes of DGPs on-line with data collection. Since the complex system can change the probabilistic mechanism of data generation, the correct statistical analysis of such data must begin with decisions about possible changes in DGPs.

Research Papers In Statistical Inference For Time Series And Related Models

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Author : Yan Liu
language : en
Publisher: Springer Nature
Release Date : 2023-05-31

Research Papers In Statistical Inference For Time Series And Related Models written by Yan Liu and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-05-31 with Mathematics categories.

This book compiles theoretical developments on statistical inference for time series and related models in honor of Masanobu Taniguchi's 70th birthday. It covers models such as long-range dependence models, nonlinear conditionally heteroscedastic time series, locally stationary processes, integer-valued time series, Lévy Processes, complex-valued time series, categorical time series, exclusive topic models, and copula models. Many cutting-edge methods such as empirical likelihood methods, quantile regression, portmanteau tests, rank-based inference, change-point detection, testing for the goodness-of-fit, higher-order asymptotic expansion, minimum contrast estimation, optimal transportation, and topological methods are proposed, considered, or applied to complex data based on the statistical inference for stochastic processes. The performances of these methods are illustrated by a variety of data analyses. This collection of original papers provides the reader with comprehensive and state-of-the-art theoretical works on time series and related models. It contains deep and profound treatments of the asymptotic theory of statistical inference. In addition, many specialized methodologies based on the asymptotic theory are presented in a simple way for a wide variety of statistical models. This Festschrift finds its core audiences in statistics, signal processing, and econometrics.

Change Point Analysis In Nonstationary Stochastic Models

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Author : Boris Brodsky
language : en
Publisher: CRC Press
Release Date : 2016-12-12

Change Point Analysis In Nonstationary Stochastic Models written by Boris Brodsky and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-12-12 with Mathematics categories.

This book covers the development of methods for detection and estimation of changes in complex systems. These systems are generally described by nonstationary stochastic models, which comprise both static and dynamic regimes, linear and nonlinear dynamics, and constant and time-variant structures of such systems. It covers both retrospective and sequential problems, particularly theoretical methods of optimal detection. Such methods are constructed and their characteristics are analyzed both theoretically and experimentally. Suitable for researchers working in change-point analysis and stochastic modelling, the book includes theoretical details combined with computer simulations and practical applications. Its rigorous approach will be appreciated by those looking to delve into the details of the methods, as well as those looking to apply them.

Data Stream Mining Processing

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Author : Sergii Babichev
language : en
Publisher: Springer Nature
Release Date : 2020-11-04

Data Stream Mining Processing written by Sergii Babichev and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-04 with Computers categories.

This book constitutes the proceedings of the third International Conference on Data Stream and Mining and Processing, DSMP 2020, held in Lviv, Ukraine*, in August 2020. The 36 full papers presented in this volume were carefully reviewed and selected from 134 submissions. The papers are organized in topical sections of ​hybrid systems of computational intelligence; machine vision and pattern recognition; dynamic data mining & data stream mining; big data & data science using intelligent approaches. *The conference was held virtually due to the COVID-19 pandemic.