Time Series Analysis For The State Space Model With R Stan

Author : Junichiro Hagiwara
Publisher : Springer Nature
Page : 350 pages
File Size : 49,6 Mb
Release : 2021-08-30
Category : Mathematics
ISBN : 9789811607110

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Time Series Analysis for the State-Space Model with R/Stan by Junichiro Hagiwara Pdf

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.

Author : James Durbin,Siem Jan Koopman
Publisher : Oxford University Press
Page : 280 pages
File Size : 51,5 Mb
Release : 2001-06-21
Category : Business & Economics
ISBN : 0198523548

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Time Series Analysis by State Space Methods by James Durbin,Siem Jan Koopman Pdf

State space time series analysis emerged in the 1960s in engineering, but its applications have spread to other fields. Durbin (statistics, London School of Economics and Political Science) and Koopman (econometrics, Free U., Amsterdam) extol the virtues of such models over the main analytical system currently used for time series data, Box-Jenkins' ARIMA. What distinguishes state space time models is that they separately model components such as trend, seasonal, regression elements and disturbance terms. Part I focuses on traditional and new techniques based on the linear Gaussian model. Part II presents new material extending the state space model to non-Gaussian observations. c. Book News Inc.

Author : James Durbin,Siem Jan Koopman
Publisher : Oxford University Press
Page : 369 pages
File Size : 51,9 Mb
Release : 2012-05-03
Category : Business & Economics
ISBN : 9780199641178

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Time Series Analysis by State Space Methods by James Durbin,Siem Jan Koopman Pdf

This is a comprehensive treatment of the state space approach to time series analysis. A distinguishing feature of state space time series models is that observations are regarded as made up of distinct components, which are each modelled separately.

Author : Masanao Aoki
Publisher : Springer Science & Business Media
Page : 339 pages
File Size : 42,8 Mb
Release : 2013-03-09
Category : Business & Economics
ISBN : 9783642758836

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State Space Modeling of Time Series by Masanao Aoki Pdf

In this book, the author adopts a state space approach to time series modeling to provide a new, computer-oriented method for building models for vector-valued time series. This second edition has been completely reorganized and rewritten. Background material leading up to the two types of estimators of the state space models is collected and presented coherently in four consecutive chapters. New, fuller descriptions are given of state space models for autoregressive models commonly used in the econometric and statistical literature. Backward innovation models are newly introduced in this edition in addition to the forward innovation models, and both are used to construct instrumental variable estimators for the model matrices. Further new items in this edition include statistical properties of the two types of estimators, more details on multiplier analysis and identification of structural models using estimated models, incorporation of exogenous signals and choice of model size. A whole new chapter is devoted to modeling of integrated, nearly integrated and co-integrated time series.

Author : Kentaro Matsuura
Publisher : Springer Nature
Page : 395 pages
File Size : 54,6 Mb
Release : 2023-01-24
Category : Computers
ISBN : 9789811947551

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Bayesian Statistical Modeling with Stan, R, and Python by Kentaro Matsuura Pdf

This book provides a highly practical introduction to Bayesian statistical modeling with Stan, which has become the most popular probabilistic programming language. The book is divided into four parts. The first part reviews the theoretical background of modeling and Bayesian inference and presents a modeling workflow that makes modeling more engineering than art. The second part discusses the use of Stan, CmdStanR, and CmdStanPy from the very beginning to basic regression analyses. The third part then introduces a number of probability distributions, nonlinear models, and hierarchical (multilevel) models, which are essential to mastering statistical modeling. It also describes a wide range of frequently used modeling techniques, such as censoring, outliers, missing data, speed-up, and parameter constraints, and discusses how to lead convergence of MCMC. Lastly, the fourth part examines advanced topics for real-world data: longitudinal data analysis, state space models, spatial data analysis, Gaussian processes, Bayesian optimization, dimensionality reduction, model selection, and information criteria, demonstrating that Stan can solve any one of these problems in as little as 30 lines. Using numerous easy-to-understand examples, the book explains key concepts, which continue to be useful when using future versions of Stan and when using other statistical modeling tools. The examples do not require domain knowledge and can be generalized to many fields. The book presents full explanations of code and math formulas, enabling readers to extend models for their own problems. All the code and data are on GitHub.

Author : Chris Chatfield,Haipeng Xing
Publisher : CRC Press
Page : 398 pages
File Size : 49,8 Mb
Release : 2019-04-25
Category : Mathematics
ISBN : 9781498795647

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The Analysis of Time Series by Chris Chatfield,Haipeng Xing Pdf

This new edition of this classic title, now in its seventh edition, presents a balanced and comprehensive introduction to the theory, implementation, and practice of time series analysis. The book covers a wide range of topics, including ARIMA models, forecasting methods, spectral analysis, linear systems, state-space models, the Kalman filters, nonlinear models, volatility models, and multivariate models. It also presents many examples and implementations of time series models and methods to reflect advances in the field. Highlights of the seventh edition: A new chapter on univariate volatility models A revised chapter on linear time series models A new section on multivariate volatility models A new section on regime switching models Many new worked examples, with R code integrated into the text The book can be used as a textbook for an undergraduate or a graduate level time series course in statistics. The book does not assume many prerequisites in probability and statistics, so it is also intended for students and data analysts in engineering, economics, and finance.

Author : Hemanta Hazarika
Publisher : Springer Nature
Page : 516 pages
File Size : 45,8 Mb
Release : 2024-06-30
Category : Electronic
ISBN : 9789819992232

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Natural Geo-Disasters and Resiliency by Hemanta Hazarika Pdf

Author : Robert H. Shumway,David S. Stoffer
Publisher : Springer
Page : 567 pages
File Size : 50,7 Mb
Release : 2017-04-25
Category : Mathematics
ISBN : 9783319524528

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Time Series Analysis and Its Applications by Robert H. Shumway,David S. Stoffer Pdf

The fourth edition of this popular graduate textbook, like its predecessors, presents a balanced and comprehensive treatment of both time and frequency domain methods with accompanying theory. Numerous examples using nontrivial data illustrate solutions to problems such as discovering natural and anthropogenic climate change, evaluating pain perception experiments using functional magnetic resonance imaging, and monitoring a nuclear test ban treaty. The book is designed as a textbook for graduate level students in the physical, biological, and social sciences and as a graduate level text in statistics. Some parts may also serve as an undergraduate introductory course. Theory and methodology are separated to allow presentations on different levels. In addition to coverage of classical methods of time series regression, ARIMA models, spectral analysis and state-space models, the text includes modern developments including categorical time series analysis, multivariate spectral methods, long memory series, nonlinear models, resampling techniques, GARCH models, ARMAX models, stochastic volatility, wavelets, and Markov chain Monte Carlo integration methods. This edition includes R code for each numerical example in addition to Appendix R, which provides a reference for the data sets and R scripts used in the text in addition to a tutorial on basic R commands and R time series. An additional file is available on the book’s website for download, making all the data sets and scripts easy to load into R.

Author : Jacques J. F. Commandeur,Siem Jan Koopman
Publisher : OUP Oxford
Page : 192 pages
File Size : 54,7 Mb
Release : 2007-07-19
Category : Business & Economics
ISBN : 9780191607806

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An Introduction to State Space Time Series Analysis by Jacques J. F. Commandeur,Siem Jan Koopman Pdf

Providing a practical introduction to state space methods as applied to unobserved components time series models, also known as structural time series models, this book introduces time series analysis using state space methodology to readers who are neither familiar with time series analysis, nor with state space methods. The only background required in order to understand the material presented in the book is a basic knowledge of classical linear regression models, of which a brief review is provided to refresh the reader's knowledge. Also, a few sections assume familiarity with matrix algebra, however, these sections may be skipped without losing the flow of the exposition. The book offers a step by step approach to the analysis of the salient features in time series such as the trend, seasonal, and irregular components. Practical problems such as forecasting and missing values are treated in some detail. This useful book will appeal to practitioners and researchers who use time series on a daily basis in areas such as the social sciences, quantitative history, biology and medicine. It also serves as an accompanying textbook for a basic time series course in econometrics and statistics, typically at an advanced undergraduate level or graduate level.

Author : Giovanni Petris,Sonia Petrone,Patrizia Campagnoli
Publisher : Springer Science & Business Media
Page : 258 pages
File Size : 47,9 Mb
Release : 2009-06-12
Category : Mathematics
ISBN : 9780387772387

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Dynamic Linear Models with R by Giovanni Petris,Sonia Petrone,Patrizia Campagnoli Pdf

State space models have gained tremendous popularity in recent years in as disparate fields as engineering, economics, genetics and ecology. After a detailed introduction to general state space models, this book focuses on dynamic linear models, emphasizing their Bayesian analysis. Whenever possible it is shown how to compute estimates and forecasts in closed form; for more complex models, simulation techniques are used. A final chapter covers modern sequential Monte Carlo algorithms. The book illustrates all the fundamental steps needed to use dynamic linear models in practice, using R. Many detailed examples based on real data sets are provided to show how to set up a specific model, estimate its parameters, and use it for forecasting. All the code used in the book is available online. No prior knowledge of Bayesian statistics or time series analysis is required, although familiarity with basic statistics and R is assumed.

Author : Nalini Ravishanker,Balaji Raman,Refik Soyer
Publisher : CRC Press
Page : 297 pages
File Size : 43,7 Mb
Release : 2022-08-10
Category : Mathematics
ISBN : 9781000622607

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Dynamic Time Series Models using R-INLA by Nalini Ravishanker,Balaji Raman,Refik Soyer Pdf

Dynamic Time Series Models using R-INLA: An Applied Perspective is the outcome of a joint effort to systematically describe the use of R-INLA for analysing time series and showcasing the code and description by several examples. This book introduces the underpinnings of R-INLA and the tools needed for modelling different types of time series using an approximate Bayesian framework. The book is an ideal reference for statisticians and scientists who work with time series data. It provides an excellent resource for teaching a course on Bayesian analysis using state space models for time series. Key Features: Introduction and overview of R-INLA for time series analysis. Gaussian and non-Gaussian state space models for time series. State space models for time series with exogenous predictors. Hierarchical models for a potentially large set of time series. Dynamic modelling of stochastic volatility and spatio-temporal dependence.

Author : Richard McElreath
Publisher : CRC Press
Page : 488 pages
File Size : 45,5 Mb
Release : 2018-01-03
Category : Mathematics
ISBN : 9781315362618

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Statistical Rethinking by Richard McElreath Pdf

Statistical Rethinking: A Bayesian Course with Examples in R and Stan builds readers’ knowledge of and confidence in statistical modeling. Reflecting the need for even minor programming in today’s model-based statistics, the book pushes readers to perform step-by-step calculations that are usually automated. This unique computational approach ensures that readers understand enough of the details to make reasonable choices and interpretations in their own modeling work. The text presents generalized linear multilevel models from a Bayesian perspective, relying on a simple logical interpretation of Bayesian probability and maximum entropy. It covers from the basics of regression to multilevel models. The author also discusses measurement error, missing data, and Gaussian process models for spatial and network autocorrelation. By using complete R code examples throughout, this book provides a practical foundation for performing statistical inference. Designed for both PhD students and seasoned professionals in the natural and social sciences, it prepares them for more advanced or specialized statistical modeling. Web Resource The book is accompanied by an R package (rethinking) that is available on the author’s website and GitHub. The two core functions (map and map2stan) of this package allow a variety of statistical models to be constructed from standard model formulas.

Author : Robert H. Shumway,David S. Stoffer
Publisher : Unknown
Page : 568 pages
File Size : 43,7 Mb
Release : 2014-01-15
Category : Electronic
ISBN : 1475732627

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Time Series Analysis and Its Applications by Robert H. Shumway,David S. Stoffer Pdf

Author : John F. Rauthmann
Publisher : Academic Press
Page : 1406 pages
File Size : 50,7 Mb
Release : 2021-01-20
Category : Psychology
ISBN : 9780128139967

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The Handbook of Personality Dynamics and Processes by John F. Rauthmann Pdf

The Handbook of Personality Dynamics and Processes is a primer to the basic and most important concepts, theories, methods, empirical findings, and applications of personality dynamics and processes. This book details how personality psychology has evolved from descriptive research to a more explanatory and dynamic science of personality, thus bridging structure- and process-based approaches, and it also reflects personality psychology’s interest in the dynamic organization and interplay of thoughts, feelings, desires, and actions within persons who are always embedded into social, cultural and historic contexts. The Handbook of Personality Dynamics and Processes tackles each topic with a range of methods geared towards assessing and analyzing their dynamic nature, such as ecological momentary sampling of personality manifestations in real-life; dynamic modeling of time-series or longitudinal personality data; network modeling and simulation; and systems-theoretical models of dynamic processes. Ties topics and methods together for a more dynamic understanding of personality Summarizes existing knowledge and insights of personality dynamics and processes Covers a broad compilation of cutting-edge insights Addresses the biophysiological and social mechanisms underlying the expression and effects of personality Examines within-person consistency and variability

Author : Jose Casals,Alfredo Garcia-Hiernaux,Miguel Jerez,Sonia Sotoca,A. Alexandre Trindade
Publisher : CRC Press
Page : 286 pages
File Size : 47,9 Mb
Release : 2018-09-03
Category : Mathematics
ISBN : 9781315360256

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State-Space Methods for Time Series Analysis by Jose Casals,Alfredo Garcia-Hiernaux,Miguel Jerez,Sonia Sotoca,A. Alexandre Trindade Pdf

The state-space approach provides a formal framework where any result or procedure developed for a basic model can be seamlessly applied to a standard formulation written in state-space form. Moreover, it can accommodate with a reasonable effort nonstandard situations, such as observation errors, aggregation constraints, or missing in-sample values. Exploring the advantages of this approach, State-Space Methods for Time Series Analysis: Theory, Applications and Software presents many computational procedures that can be applied to a previously specified linear model in state-space form. After discussing the formulation of the state-space model, the book illustrates the flexibility of the state-space representation and covers the main state estimation algorithms: filtering and smoothing. It then shows how to compute the Gaussian likelihood for unknown coefficients in the state-space matrices of a given model before introducing subspace methods and their application. It also discusses signal extraction, describes two algorithms to obtain the VARMAX matrices corresponding to any linear state-space model, and addresses several issues relating to the aggregation and disaggregation of time series. The book concludes with a cross-sectional extension to the classical state-space formulation in order to accommodate longitudinal or panel data. Missing data is a common occurrence here, and the book explains imputation procedures necessary to treat missingness in both exogenous and endogenous variables. Web Resource The authors’ E4 MATLAB® toolbox offers all the computational procedures, administrative and analytical functions, and related materials for time series analysis. This flexible, powerful, and free software tool enables readers to replicate the practical examples in the text and apply the procedures to their own work.