Download or read book Multivariate Time Series With Linear State Space Structure written by Víctor Gómez and published by Springer. This book was released on 2016-05-09 with total page 553 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a comprehensive study of multivariate time series with linear state space structure. The emphasis is put on both the clarity of the theoretical concepts and on efficient algorithms for implementing the theory. In particular, it investigates the relationship between VARMA and state space models, including canonical forms. It also highlights the relationship between Wiener-Kolmogorov and Kalman filtering both with an infinite and a finite sample. The strength of the book also lies in the numerous algorithms included for state space models that take advantage of the recursive nature of the models. Many of these algorithms can be made robust, fast, reliable and efficient. The book is accompanied by a MATLAB package called SSMMATLAB and a webpage presenting implemented algorithms with many examples and case studies. Though it lays a solid theoretical foundation, the book also focuses on practical application, and includes exercises in each chapter. It is intended for researchers and students working with linear state space models, and who are familiar with linear algebra and possess some knowledge of statistics.

Download or read book State Space and Unobserved Component Models written by James Durbin and published by Cambridge University Press. This book was released on 2004-06-10 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive overview of developments in the theory and application of state space modeling, first published in 2004.

Download or read book Forecasting Structural Time Series Models and the Kalman Filter written by Andrew C. Harvey and published by Cambridge University Press. This book was released on 1990 with total page 574 pages. Available in PDF, EPUB and Kindle. Book excerpt: A synthesis of concepts and materials, that ordinarily appear separately in time series and econometrics literature, presents a comprehensive review of theoretical and applied concepts in modeling economic and social time series.

Download or read book Time Series Analysis by State Space Methods written by James Durbin and published by Oxford University Press. This book was released on 2001-06-21 with total page 280 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Elements of Multivariate Time Series Analysis written by Gregory C. Reinsel and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of methods of time series analysis in the study of multivariate time series has become of increased interest in recent years. Although the methods are rather well developed and understood for univarjate time series analysis, the situation is not so complete for the multivariate case. This book is designed to introduce the basic concepts and methods that are useful in the analysis and modeling of multivariate time series, with illustrations of these basic ideas. The development includes both traditional topics such as autocovariance and auto correlation matrices of stationary processes, properties of vector ARMA models, forecasting ARMA processes, least squares and maximum likelihood estimation techniques for vector AR and ARMA models, and model checking diagnostics for residuals, as well as topics of more recent interest for vector ARMA models such as reduced rank structure, structural indices, scalar component models, canonical correlation analyses for vector time series, multivariate unit-root models and cointegration structure, and state-space models and Kalman filtering techniques and applications. This book concentrates on the time-domain analysis of multivariate time series, and the important subject of spectral analysis is not considered here. For that topic, the reader is referred to the excellent books by Jenkins and Watts (1968), Hannan (1970), Priestley (1981), and others.

Download or read book Dynamic Linear Models with R written by Giovanni Petris and published by Springer Science & Business Media. This book was released on 2009-06-12 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Harmonies of Disorder written by Leone Montagnini and published by Springer. This book was released on 2017-08-19 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the entire body of thought of Norbert Wiener (1894–1964), knowledge of which is essential if one wishes to understand and correctly interpret the age in which we live. The focus is in particular on the philosophical and sociological aspects of Wiener’s thought, but these aspects are carefully framed within the context of his scientific journey. Important biographical events, including some that were previously unknown, are also highlighted, but while the book has a biographical structure, it is not only a biography. The book is divided into four chronological sections, the first two of which explore Wiener’s development as a philosopher and logician and his brilliant interwar career as a mathematician, supported by his philosophical background. The third section considers his research during World War II, which drew upon his previous scientific work and reflections and led to the birth of cybernetics. Finally, the radical post-war shift in Wiener’s intellectual path is considered, examining how he came to abandon computer science projects and commenced ceaseless public reflections on the new sciences and technologies of information, their social effects, and the need for responsibility in science.

Download or read book Time Series Analysis by State Space Methods written by James Durbin and published by OUP Oxford. This book was released on 2012-05-03 with total page 369 pages. Available in PDF, EPUB and Kindle. Book excerpt: This new edition updates Durbin & Koopman's important text on the state space approach to time series analysis. The distinguishing feature of state space time series models is that observations are regarded as made up of distinct components such as trend, seasonal, regression elements and disturbance terms, each of which is modelled separately. The techniques that emerge from this approach are very flexible and are capable of handling a much wider range of problems than the main analytical system currently in use for time series analysis, the Box-Jenkins ARIMA system. Additions to this second edition include the filtering of nonlinear and non-Gaussian series. Part I of the book obtains the mean and variance of the state, of a variable intended to measure the effect of an interaction and of regression coefficients, in terms of the observations. Part II extends the treatment to nonlinear and non-normal models. For these, analytical solutions are not available so methods are based on simulation.

Download or read book Smoothness Priors Analysis of Time Series written by Genshiro Kitagawa and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 265 pages. Available in PDF, EPUB and Kindle. Book excerpt: Smoothness Priors Analysis of Time Series addresses some of the problems of modeling stationary and nonstationary time series primarily from a Bayesian stochastic regression "smoothness priors" state space point of view. Prior distributions on model coefficients are parametrized by hyperparameters. Maximizing the likelihood of a small number of hyperparameters permits the robust modeling of a time series with relatively complex structure and a very large number of implicitly inferred parameters. The critical statistical ideas in smoothness priors are the likelihood of the Bayesian model and the use of likelihood as a measure of the goodness of fit of the model. The emphasis is on a general state space approach in which the recursive conditional distributions for prediction, filtering, and smoothing are realized using a variety of nonstandard methods including numerical integration, a Gaussian mixture distribution-two filter smoothing formula, and a Monte Carlo "particle-path tracing" method in which the distributions are approximated by many realizations. The methods are applicable for modeling time series with complex structures.

Download or read book Advanced State Space Methods for Neural and Clinical Data written by Zhe Chen and published by Cambridge University Press. This book was released on 2015-10-15 with total page 397 pages. Available in PDF, EPUB and Kindle. Book excerpt: This authoritative work provides an in-depth treatment of state space methods, with a range of applications in neural and clinical data. Advanced and state-of-the-art research topics are detailed, including topics in state space analyses, maximum likelihood methods, variational Bayes, sequential Monte Carlo, Markov chain Monte Carlo, nonparametric Bayesian, and deep learning methods. Details are provided on practical applications in neural and clinical data, whether this is characterising time series data from neural spike trains recorded from the rat hippocampus, the primate motor cortex, or the human EEG, MEG or fMRI, or physiological measurements of heartbeats or blood pressures. With real-world case studies of neuroscience experiments and clinical data sets, and written by expert authors from across the field, this is an ideal resource for anyone working in neuroscience and physiological data analysis.

Download or read book Multidimensional Stationary Time Series written by Marianna Bolla and published by CRC Press. This book was released on 2021-04-29 with total page 318 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gives a brief survey of the theory of multidimensional (multivariate), weakly stationary time series, with emphasis on dimension reduction and prediction. Understanding the covered material requires a certain mathematical maturity, a degree of knowledge in probability theory, linear algebra, and also in real, complex and functional analysis. For this, the cited literature and the Appendix contain all necessary material. The main tools of the book include harmonic analysis, some abstract algebra, and state space methods: linear time-invariant filters, factorization of rational spectral densities, and methods that reduce the rank of the spectral density matrix. Serves to find analogies between classical results (Cramer, Wold, Kolmogorov, Wiener, Kálmán, Rozanov) and up-to-date methods for dimension reduction in multidimensional time series Provides a unified treatment for time and frequency domain inferences by using machinery of complex and harmonic analysis, spectral and Smith--McMillan decompositions. Establishes analogies between the time and frequency domain notions and calculations Discusses the Wold's decomposition and the Kolmogorov's classification together, by distinguishing between different types of singularities. Understanding the remote past helps us to characterize the ideal situation where there is a regular part at present. Examples and constructions are also given Establishes a common outline structure for the state space models, prediction, and innovation algorithms with unified notions and principles, which is applicable to real-life high frequency time series It is an ideal companion for graduate students studying the theory of multivariate time series and researchers working in this field.

Download or read book Statistical Methods in Epilepsy written by Sharon Chiang and published by CRC Press. This book was released on 2024-03-25 with total page 419 pages. Available in PDF, EPUB and Kindle. Book excerpt: Epilepsy research promises new treatments and insights into brain function, but statistics and machine learning are paramount for extracting meaning from data and enabling discovery. Statistical Methods in Epilepsy provides a comprehensive introduction to statistical methods used in epilepsy research. Written in a clear, accessible style by leading authorities, this textbook demystifies introductory and advanced statistical methods, providing a practical roadmap that will be invaluable for learners and experts alike. Topics include a primer on version control and coding, pre-processing of imaging and electrophysiological data, hypothesis testing, generalized linear models, survival analysis, network analysis, time-series analysis, spectral analysis, spatial statistics, unsupervised and supervised learning, natural language processing, prospective trial design, pharmacokinetic and pharmacodynamic modeling, and randomized clinical trials. Features: Provides a comprehensive introduction to statistical methods employed in epilepsy research Divided into four parts: Basic Processing Methods for Data Analysis; Statistical Models for Epilepsy Data Types; Machine Learning Methods; and Clinical Studies Covers methodological and practical aspects, as well as worked-out examples with R and Python code provided in the online supplement Includes contributions by experts in the field https://github.com/sharon-chiang/Statistics-Epilepsy-Book/ The handbook targets clinicians, graduate students, medical students, and researchers who seek to conduct quantitative epilepsy research. The topics covered extend broadly to quantitative research in other neurological specialties and provide a valuable reference for the field of neurology.

Download or read book A Course in Time Series Analysis written by Daniel Peña and published by John Wiley & Sons. This book was released on 2011-01-25 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: New statistical methods and future directions of research in time series A Course in Time Series Analysis demonstrates how to build time series models for univariate and multivariate time series data. It brings together material previously available only in the professional literature and presents a unified view of the most advanced procedures available for time series model building. The authors begin with basic concepts in univariate time series, providing an up-to-date presentation of ARIMA models, including the Kalman filter, outlier analysis, automatic methods for building ARIMA models, and signal extraction. They then move on to advanced topics, focusing on heteroscedastic models, nonlinear time series models, Bayesian time series analysis, nonparametric time series analysis, and neural networks. Multivariate time series coverage includes presentations on vector ARMA models, cointegration, and multivariate linear systems. Special features include: Contributions from eleven of the worldâ??s leading figures in time series Shared balance between theory and application Exercise series sets Many real data examples Consistent style and clear, common notation in all contributions 60 helpful graphs and tables Requiring no previous knowledge of the subject, A Course in Time Series Analysis is an important reference and a highly useful resource for researchers and practitioners in statistics, economics, business, engineering, and environmental analysis. An Instructor's Manual presenting detailed solutions to all the problems in he book is available upon request from the Wiley editorial department.

Download or read book Quantitative Methods for Portfolio Analysis written by T. Kariya and published by Springer Science & Business Media. This book was released on 2012-12-06 with total page 321 pages. Available in PDF, EPUB and Kindle. Book excerpt: Quantitative Methods for Portfolio Analysis provides practical models and methods for the quantitative analysis of financial asset prices, construction of various portfolios, and computer-assisted trading systems. In particular, this book is required reading for: (1) `Quants' (quantitatively-inclined analysts) in financial industries; (2) financial engineers in investment banks, securities companies, derivative-trading companies, software houses, etc., who are developing portfolio trading systems; (3) graduate students and specialists in the areas of finance, business, economics, statistics, financial engineering; and (4) investors who are interested in Japanese financial markets. Throughout the book the emphasis is placed on the originality and usefulness of models and methods for the construction of portfolios and investment decision making, and examples are provided to demonstrate, with practical analysis, models for Japanese financial markets.

Download or read book Elements of Multivariate Time Series Analysis written by Gregory C. Reinsel and published by Springer Science & Business Media. This book was released on 2003-10-31 with total page 384 pages. Available in PDF, EPUB and Kindle. Book excerpt: Now available in paperback, this book introduces basic concepts and methods useful in the analysis and modeling of multivariate time series data. It concentrates on the time-domain analysis of multivariate time series, and assumes univariate time series analysis, while covering basic topics such as stationary processes and their covariance matrix structure, vector AR, MA, and ARMA models, forecasting, least squares and maximum likelihood estimation for ARMA models, associated likelihood ratio testing procedures.

Download or read book An Introduction to State Space Time Series Analysis written by Jacques J. F. Commandeur and published by OUP Oxford. This book was released on 2007-07-19 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Methods in Brain Connectivity Inference through Multivariate Time Series Analysis written by Koichi Sameshima and published by CRC Press. This book was released on 2016-04-19 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Interest in brain connectivity inference has become ubiquitous and is now increasingly adopted in experimental investigations of clinical, behavioral, and experimental neurosciences. Methods in Brain Connectivity Inference through Multivariate Time Series Analysis gathers the contributions of leading international authors who discuss different time