Handbook Of Discrete Valued Time Series

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Handbook of Discrete-Valued Time Series

Richard A. Davis,Scott H. Holan,Robert Lund,Nalini Ravishanker
Handbook of Discrete Valued Time Series Book PDF

Author : Richard A. Davis,Scott H. Holan,Robert Lund,Nalini Ravishanker
Publisher : CRC Press
Page : 484 pages
File Size : 47,9 Mb
Release : 2016-01-06
Category : Mathematics
ISBN : 9781466577749

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Handbook of Discrete-Valued Time Series by Richard A. Davis,Scott H. Holan,Robert Lund,Nalini Ravishanker Pdf

Model a Wide Range of Count Time Series Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed ca

An Introduction to Discrete-Valued Time Series

Christian H. Weiss
An Introduction to Discrete Valued Time Series Book PDF

Author : Christian H. Weiss
Publisher : John Wiley & Sons
Page : 300 pages
File Size : 47,9 Mb
Release : 2018-02-05
Category : Mathematics
ISBN : 9781119096962

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An Introduction to Discrete-Valued Time Series by Christian H. Weiss Pdf

A much-needed introduction to the field of discrete-valued time series, with a focus on count-data time series Time series analysis is an essential tool in a wide array of fields, including business, economics, computer science, epidemiology, finance, manufacturing and meteorology, to name just a few. Despite growing interest in discrete-valued time series—especially those arising from counting specific objects or events at specified times—most books on time series give short shrift to that increasingly important subject area. This book seeks to rectify that state of affairs by providing a much needed introduction to discrete-valued time series, with particular focus on count-data time series. The main focus of this book is on modeling. Throughout numerous examples are provided illustrating models currently used in discrete-valued time series applications. Statistical process control, including various control charts (such as cumulative sum control charts), and performance evaluation are treated at length. Classic approaches like ARMA models and the Box-Jenkins program are also featured with the basics of these approaches summarized in an Appendix. In addition, data examples, with all relevant R code, are available on a companion website. Provides a balanced presentation of theory and practice, exploring both categorical and integer-valued series Covers common models for time series of counts as well as for categorical time series, and works out their most important stochastic properties Addresses statistical approaches for analyzing discrete-valued time series and illustrates their implementation with numerous data examples Covers classical approaches such as ARMA models, Box-Jenkins program and how to generate functions Includes dataset examples with all necessary R code provided on a companion website An Introduction to Discrete-Valued Time Series is a valuable working resource for researchers and practitioners in a broad range of fields, including statistics, data science, machine learning, and engineering. It will also be of interest to postgraduate students in statistics, mathematics and economics.

Hidden Markov and Other Models for Discrete- valued Time Series

Iain L. MacDonald,Walter Zucchini
Hidden Markov and Other Models for Discrete valued Time Series Book PDF

Author : Iain L. MacDonald,Walter Zucchini
Publisher : CRC Press
Page : 256 pages
File Size : 51,5 Mb
Release : 1997-01-01
Category : Mathematics
ISBN : 0412558505

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Hidden Markov and Other Models for Discrete- valued Time Series by Iain L. MacDonald,Walter Zucchini Pdf

Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time Series introduces a new, versatile, and computationally tractable class of models, the "hidden Markov" models. It presents a detailed account of these models, then applies them to data from a wide range of diverse subject areas, including medicine, climatology, and geophysics. This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics. Researchers and applied statisticians who analyze time series data in medicine, animal behavior, hydrology, and sociology will also find this information useful.

Copula-Based Markov Models for Time Series

Li-Hsien Sun,Xin-Wei Huang,Mohammed S. Alqawba,Jong-Min Kim,Takeshi Emura
Copula Based Markov Models for Time Series Book PDF

Author : Li-Hsien Sun,Xin-Wei Huang,Mohammed S. Alqawba,Jong-Min Kim,Takeshi Emura
Publisher : Springer Nature
Page : 141 pages
File Size : 47,5 Mb
Release : 2020-07-01
Category : Business & Economics
ISBN : 9789811549984

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Copula-Based Markov Models for Time Series by Li-Hsien Sun,Xin-Wei Huang,Mohammed S. Alqawba,Jong-Min Kim,Takeshi Emura Pdf

This book provides statistical methodologies for time series data, focusing on copula-based Markov chain models for serially correlated time series. It also includes data examples from economics, engineering, finance, sport and other disciplines to illustrate the methods presented. An accessible textbook for students in the fields of economics, management, mathematics, statistics, and related fields wanting to gain insights into the statistical analysis of time series data using copulas, the book also features stand-alone chapters to appeal to researchers. As the subtitle suggests, the book highlights parametric models based on normal distribution, t-distribution, normal mixture distribution, Poisson distribution, and others. Presenting likelihood-based methods as the main statistical tools for fitting the models, the book details the development of computing techniques to find the maximum likelihood estimator. It also addresses statistical process control, as well as Bayesian and regression methods. Lastly, to help readers analyze their data, it provides computer codes (R codes) for most of the statistical methods.

Discrete-Valued Time Series

Christian H Weiss
Discrete Valued Time Series Book PDF

Author : Christian H Weiss
Publisher : Unknown
Page : 0 pages
File Size : 41,7 Mb
Release : 2024-03-12
Category : Mathematics
ISBN : 372580477X

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Discrete-Valued Time Series by Christian H Weiss Pdf

The analysis and modeling of time series has been an active research area for more than 100 years, with the main focus on time series having a continuous range consisting of real numbers or real vectors. It took until the 1980s for the first papers on discrete-valued time series to appear. In the 2000s, a rapid increase in research activity was noted, but only in the last few years was a certain maturity and consolidation of the area of discrete-valued time series observed. This reprint is a collection of articles on a wide range of topics on discrete-valued time series (especially count time series), covering stochastic models and methods for their analysis, univariate and multivariate time series, applications of time series methods to risk analysis, statistical process control, and many more. The proposed approaches and concepts are thoroughly discussed and illustrated with several real-world data examples.

Advances in Time Series Analysis and Forecasting

Ignacio Rojas,Héctor Pomares,Olga Valenzuela
Advances in Time Series Analysis and Forecasting Book PDF

Author : Ignacio Rojas,Héctor Pomares,Olga Valenzuela
Publisher : Springer
Page : 414 pages
File Size : 50,8 Mb
Release : 2017-07-31
Category : Business & Economics
ISBN : 9783319557892

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Advances in Time Series Analysis and Forecasting by Ignacio Rojas,Héctor Pomares,Olga Valenzuela Pdf

This volume of selected and peer-reviewed contributions on the latest developments in time series analysis and forecasting updates the reader on topics such as analysis of irregularly sampled time series, multi-scale analysis of univariate and multivariate time series, linear and non-linear time series models, advanced time series forecasting methods, applications in time series analysis and forecasting, advanced methods and online learning in time series and high-dimensional and complex/big data time series. The contributions were originally presented at the International Work-Conference on Time Series, ITISE 2016, held in Granada, Spain, June 27-29, 2016. The series of ITISE conferences provides a forum for scientists, engineers, educators and students to discuss the latest ideas and implementations in the foundations, theory, models and applications in the field of time series analysis and forecasting. It focuses on interdisciplinary and multidisciplinary research encompassing the disciplines of computer science, mathematics, statistics and econometrics.

Bayesian Analysis of Time Series

Lyle D. Broemeling
Bayesian Analysis of Time Series Book PDF

Author : Lyle D. Broemeling
Publisher : CRC Press
Page : 211 pages
File Size : 52,7 Mb
Release : 2019-04-16
Category : Mathematics
ISBN : 9780429948916

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Bayesian Analysis of Time Series by Lyle D. Broemeling Pdf

In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.

Bayesian Analysis of Infectious Diseases

Lyle D. Broemeling
Bayesian Analysis of Infectious Diseases Book PDF

Author : Lyle D. Broemeling
Publisher : CRC Press
Page : 216 pages
File Size : 50,6 Mb
Release : 2021-02-08
Category : Mathematics
ISBN : 9781000336474

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Bayesian Analysis of Infectious Diseases by Lyle D. Broemeling Pdf

Bayesian Analysis of Infectious Diseases -COVID-19 and Beyond shows how the Bayesian approach can be used to analyze the evolutionary behavior of infectious diseases, including the coronavirus pandemic. The book describes the foundation of Bayesian statistics while explicating the biology and evolutionary behavior of infectious diseases, including viral and bacterial manifestations of the contagion. The book discusses the application of Markov Chains to contagious diseases, previews data analysis models, the epidemic threshold theorem, and basic properties of the infection process. Also described are the chain binomial model for the evolution of epidemics. Features: Represents the first book on infectious disease from a Bayesian perspective. Employs WinBUGS and R to generate observations that follow the course of contagious maladies. Includes discussion of the coronavirus pandemic as well as many examples from the past, including the flu epidemic of 1918-1919. Compares standard non-Bayesian and Bayesian inferences. Offers a companion website with the R and WinBUGS code.

Stochastic Models, Statistics and Their Applications

Ansgar Steland,Ewaryst Rafajłowicz,Ostap Okhrin
Stochastic Models Statistics and Their Applications Book PDF

Author : Ansgar Steland,Ewaryst Rafajłowicz,Ostap Okhrin
Publisher : Springer Nature
Page : 450 pages
File Size : 45,7 Mb
Release : 2019-10-15
Category : Mathematics
ISBN : 9783030286651

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Stochastic Models, Statistics and Their Applications by Ansgar Steland,Ewaryst Rafajłowicz,Ostap Okhrin Pdf

This volume presents selected and peer-reviewed contributions from the 14th Workshop on Stochastic Models, Statistics and Their Applications, held in Dresden, Germany, on March 6-8, 2019. Addressing the needs of theoretical and applied researchers alike, the contributions provide an overview of the latest advances and trends in the areas of mathematical statistics and applied probability, and their applications to high-dimensional statistics, econometrics and time series analysis, statistics for stochastic processes, statistical machine learning, big data and data science, random matrix theory, quality control, change-point analysis and detection, finance, copulas, survival analysis and reliability, sequential experiments, empirical processes, and microsimulations. As the book demonstrates, stochastic models and related statistical procedures and algorithms are essential to more comprehensively understanding and solving present-day problems arising in e.g. the natural sciences, machine learning, data science, engineering, image analysis, genetics, econometrics and finance.

Linear Models and Time-Series Analysis

Marc S. Paolella
Linear Models and Time Series Analysis Book PDF

Author : Marc S. Paolella
Publisher : John Wiley & Sons
Page : 896 pages
File Size : 52,5 Mb
Release : 2018-10-10
Category : Mathematics
ISBN : 9781119431855

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Linear Models and Time-Series Analysis by Marc S. Paolella Pdf

A comprehensive and timely edition on an emerging new trend in time series Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH sets a strong foundation, in terms of distribution theory, for the linear model (regression and ANOVA), univariate time series analysis (ARMAX and GARCH), and some multivariate models associated primarily with modeling financial asset returns (copula-based structures and the discrete mixed normal and Laplace). It builds on the author's previous book, Fundamental Statistical Inference: A Computational Approach, which introduced the major concepts of statistical inference. Attention is explicitly paid to application and numeric computation, with examples of Matlab code throughout. The code offers a framework for discussion and illustration of numerics, and shows the mapping from theory to computation. The topic of time series analysis is on firm footing, with numerous textbooks and research journals dedicated to it. With respect to the subject/technology, many chapters in Linear Models and Time-Series Analysis cover firmly entrenched topics (regression and ARMA). Several others are dedicated to very modern methods, as used in empirical finance, asset pricing, risk management, and portfolio optimization, in order to address the severe change in performance of many pension funds, and changes in how fund managers work. Covers traditional time series analysis with new guidelines Provides access to cutting edge topics that are at the forefront of financial econometrics and industry Includes latest developments and topics such as financial returns data, notably also in a multivariate context Written by a leading expert in time series analysis Extensively classroom tested Includes a tutorial on SAS Supplemented with a companion website containing numerous Matlab programs Solutions to most exercises are provided in the book Linear Models and Time-Series Analysis: Regression, ANOVA, ARMA and GARCH is suitable for advanced masters students in statistics and quantitative finance, as well as doctoral students in economics and finance. It is also useful for quantitative financial practitioners in large financial institutions and smaller finance outlets.

GARCH Models

Christian Francq,Jean-Michel Zakoian
GARCH Models Book PDF

Author : Christian Francq,Jean-Michel Zakoian
Publisher : John Wiley & Sons
Page : 504 pages
File Size : 42,6 Mb
Release : 2019-03-19
Category : Mathematics
ISBN : 9781119313564

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GARCH Models by Christian Francq,Jean-Michel Zakoian Pdf

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.

Advanced Data Analysis in Neuroscience

Daniel Durstewitz
Advanced Data Analysis in Neuroscience Book PDF

Author : Daniel Durstewitz
Publisher : Springer
Page : 292 pages
File Size : 42,9 Mb
Release : 2017-09-15
Category : Medical
ISBN : 9783319599762

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Advanced Data Analysis in Neuroscience by Daniel Durstewitz Pdf

This book is intended for use in advanced graduate courses in statistics / machine learning, as well as for all experimental neuroscientists seeking to understand statistical methods at a deeper level, and theoretical neuroscientists with a limited background in statistics. It reviews almost all areas of applied statistics, from basic statistical estimation and test theory, linear and nonlinear approaches for regression and classification, to model selection and methods for dimensionality reduction, density estimation and unsupervised clustering. Its focus, however, is linear and nonlinear time series analysis from a dynamical systems perspective, based on which it aims to convey an understanding also of the dynamical mechanisms that could have generated observed time series. Further, it integrates computational modeling of behavioral and neural dynamics with statistical estimation and hypothesis testing. This way computational models in neuroscience are not only explanatory frameworks, but become powerful, quantitative data-analytical tools in themselves that enable researchers to look beyond the data surface and unravel underlying mechanisms. Interactive examples of most methods are provided through a package of MatLab routines, encouraging a playful approach to the subject, and providing readers with a better feel for the practical aspects of the methods covered. "Computational neuroscience is essential for integrating and providing a basis for understanding the myriads of remarkable laboratory data on nervous system functions. Daniel Durstewitz has excellently covered the breadth of computational neuroscience from statistical interpretations of data to biophysically based modeling of the neurobiological sources of those data. His presentation is clear, pedagogically sound, and readily useable by experts and beginners alike. It is a pleasure to recommend this very well crafted discussion to experimental neuroscientists as well as mathematically well versed Physicists. The book acts as a window to the issues, to the questions, and to the tools for finding the answers to interesting inquiries about brains and how they function." Henry D. I. Abarbanel Physics and Scripps Institution of Oceanography, University of California, San Diego “This book delivers a clear and thorough introduction to sophisticated analysis approaches useful in computational neuroscience. The models described and the examples provided will help readers develop critical intuitions into what the methods reveal about data. The overall approach of the book reflects the extensive experience Prof. Durstewitz has developed as a leading practitioner of computational neuroscience. “ Bruno B. Averbeck

Bayesian Hierarchical Models

Peter D. Congdon
Bayesian Hierarchical Models Book PDF

Author : Peter D. Congdon
Publisher : CRC Press
Page : 506 pages
File Size : 51,9 Mb
Release : 2019-09-16
Category : Mathematics
ISBN : 9780429532900

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Bayesian Hierarchical Models by Peter D. Congdon Pdf

An intermediate-level treatment of Bayesian hierarchical models and their applications, this book demonstrates the advantages of a Bayesian approach to data sets involving inferences for collections of related units or variables, and in methods where parameters can be treated as random collections. Through illustrative data analysis and attention to statistical computing, this book facilitates practical implementation of Bayesian hierarchical methods. The new edition is a revision of the book Applied Bayesian Hierarchical Methods. It maintains a focus on applied modelling and data analysis, but now using entirely R-based Bayesian computing options. It has been updated with a new chapter on regression for causal effects, and one on computing options and strategies. This latter chapter is particularly important, due to recent advances in Bayesian computing and estimation, including the development of rjags and rstan. It also features updates throughout with new examples. The examples exploit and illustrate the broader advantages of the R computing environment, while allowing readers to explore alternative likelihood assumptions, regression structures, and assumptions on prior densities. Features: Provides a comprehensive and accessible overview of applied Bayesian hierarchical modelling Includes many real data examples to illustrate different modelling topics R code (based on rjags, jagsUI, R2OpenBUGS, and rstan) is integrated into the book, emphasizing implementation Software options and coding principles are introduced in new chapter on computing Programs and data sets available on the book’s website

Matrices, Statistics and Big Data

S. Ejaz Ahmed,Francisco Carvalho,Simo Puntanen
Matrices Statistics and Big Data Book PDF

Author : S. Ejaz Ahmed,Francisco Carvalho,Simo Puntanen
Publisher : Springer
Page : 190 pages
File Size : 49,6 Mb
Release : 2019-08-02
Category : Mathematics
ISBN : 9783030175191

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Matrices, Statistics and Big Data by S. Ejaz Ahmed,Francisco Carvalho,Simo Puntanen Pdf

This volume features selected, refereed papers on various aspects of statistics, matrix theory and its applications to statistics, as well as related numerical linear algebra topics and numerical solution methods, which are relevant for problems arising in statistics and in big data. The contributions were originally presented at the 25th International Workshop on Matrices and Statistics (IWMS 2016), held in Funchal (Madeira), Portugal on June 6-9, 2016. The IWMS workshop series brings together statisticians, computer scientists, data scientists and mathematicians, helping them better understand each other’s tools, and fostering new collaborations at the interface of matrix theory and statistics.

Handbook of Mixture Analysis

Sylvia Fruhwirth-Schnatter,Gilles Celeux,Christian P. Robert
Handbook of Mixture Analysis Book PDF

Author : Sylvia Fruhwirth-Schnatter,Gilles Celeux,Christian P. Robert
Publisher : CRC Press
Page : 388 pages
File Size : 42,7 Mb
Release : 2019-01-04
Category : Computers
ISBN : 9780429508868

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Handbook of Mixture Analysis by Sylvia Fruhwirth-Schnatter,Gilles Celeux,Christian P. Robert Pdf

Mixture models have been around for over 150 years, and they are found in many branches of statistical modelling, as a versatile and multifaceted tool. They can be applied to a wide range of data: univariate or multivariate, continuous or categorical, cross-sectional, time series, networks, and much more. Mixture analysis is a very active research topic in statistics and machine learning, with new developments in methodology and applications taking place all the time. The Handbook of Mixture Analysis is a very timely publication, presenting a broad overview of the methods and applications of this important field of research. It covers a wide array of topics, including the EM algorithm, Bayesian mixture models, model-based clustering, high-dimensional data, hidden Markov models, and applications in finance, genomics, and astronomy. Features: Provides a comprehensive overview of the methods and applications of mixture modelling and analysis Divided into three parts: Foundations and Methods; Mixture Modelling and Extensions; and Selected Applications Contains many worked examples using real data, together with computational implementation, to illustrate the methods described Includes contributions from the leading researchers in the field The Handbook of Mixture Analysis is targeted at graduate students and young researchers new to the field. It will also be an important reference for anyone working in this field, whether they are developing new methodology, or applying the models to real scientific problems.