State Space Modeling Of Time Series
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State Space Modeling Of Time Series
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Author : Masanao Aoki
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-03-09
State Space Modeling Of Time Series written by Masanao Aoki 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 2013-03-09 with Business & Economics categories.
model's predictive capability? These are some of the questions that need to be answered in proposing any time series model construction method. This book addresses these questions in Part II. Briefly, the covariance matrices between past data and future realizations of time series are used to build a matrix called the Hankel matrix. Information needed for constructing models is extracted from the Hankel matrix. For example, its numerically determined rank will be the di mension of the state model. Thus the model dimension is determined by the data, after balancing several sources of error for such model construction. The covariance matrix of the model forecasting error vector is determined by solving a certain matrix Riccati equation. This matrix is also the covariance matrix of the innovation process which drives the model in generating model forecasts. In these model construction steps, a particular model representation, here referred to as balanced, is used extensively. This mode of model representation facilitates error analysis, such as assessing the error of using a lower dimensional model than that indicated by the rank of the Hankel matrix. The well-known Akaike's canonical correlation method for model construc tion is similar to the one used in this book. There are some important differ ences, however. Akaike uses the normalized Hankel matrix to extract canonical vectors, while the method used in this book does not normalize the Hankel ma trix.
Time Series Analysis By State Space Methods
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Author : James Durbin
language : en
Publisher: Oxford University Press
Release Date : 2001-06-21
Time Series Analysis By State Space Methods written by James Durbin and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2001-06-21 with Business & Economics categories.
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.
Essentials Of Time Series Econometrics
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Author : Rajat Chopra
language : en
Publisher: Educohack Press
Release Date : 2025-02-20
Essentials Of Time Series Econometrics written by Rajat Chopra and has been published by Educohack Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-02-20 with Science categories.
"Essentials of Time Series Econometrics" explores the fundamental principles, methodologies, and practical applications of time series analysis in economics, finance, and related fields. Designed for students, researchers, and practitioners, this guide covers both theoretical foundations and practical techniques used to analyze temporal data and make informed decisions. We cover a wide range of topics, including basic concepts such as stationarity and autocorrelation, as well as advanced techniques like machine learning approaches, Bayesian analysis, and high-frequency data analysis. Each chapter provides clear explanations of key concepts, methodologies, and mathematical principles. Real-world examples and case studies illustrate the application of time series analysis in various domains. Hands-on exercises and practical assignments reinforce understanding and develop analytical skills. Contributions from leading experts ensure readers benefit from the latest research findings. A companion website offers additional resources, including datasets, code examples, and supplementary materials. This book is ideal for students, researchers, and practitioners looking to build a solid foundation in time series econometrics or apply advanced techniques to real-world problems.
Time Series Analysis And Applications To Geophysical Systems
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Author : David Brillinger
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Time Series Analysis And Applications To Geophysical Systems written by David Brillinger 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.
Part of a two volume set based on a recent IMA program of the same name. The goal of the program and these books is to develop a community of statistical and other scientists kept up-to-date on developments in this quickly evolving and interdisciplinary field. Consequently, these books present recent material by distinguished researchers. Topics discussed in Part I include nonlinear and non- Gaussian models and processes (higher order moments and spectra, nonlinear systems, applications in astronomy, geophysics, engineering, and simulation) and the interaction of time series analysis and statistics (information model identification, categorical valued time series, nonparametric and semiparametric methods). Self-similar processes and long-range dependence (time series with long memory, fractals, 1/f noise, stable noise) and time series research common to engineers and economists (modeling of multivariate and possibly non-stationary time series, state space and adaptive methods) are discussed in Part II.
Handbook Of Intraindividual Variability Across The Life Span
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Author : Manfred Diehl
language : en
Publisher: Routledge
Release Date : 2014-12-08
Handbook Of Intraindividual Variability Across The Life Span written by Manfred Diehl and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-12-08 with Psychology categories.
Intraindividual variability (IIV) of human development and behavior across the entire life-span is explored in this new book. Leading researchers summarize recent findings on the extent, role, and function of IIV in human development with a focus on how, when, and why individuals change over time. The latest theoretical, methodological, and technological advances are reviewed. The book explores the historical and theoretical background and challenges of IIV research along with its role and function in childhood, adolescence, and adulthood. Edited to maximize consistency and accessibility, each chapter includes an introduction and a review of the research and most explore future directions, new theoretical developments, and conclusions and implications. Readers are shown that by focusing on the individual as a unit of analysis across different time scales, conditions, and situations, researchers can effectively demonstrate behavioral and developmental regularities at different points of the life-span. As such this book is a must have for anybody interested in IIV research. The book explores: -New designs and methods for the analysis of intensive repeated measures data. -The importance of real-time data for more time sensitive and ecologically valid measurements. -The role and function of intraindividual variability in behavior and development across the life-span -- from infancy to later life. -Numerous examples of how intraindividual variability research is conducted. -Topics and findings that are commonly treated in disparate bodies of literature from various disciplines. Part 1 provides a historical, conceptual, and methodological overview of the study of intraindividual variability (IIV). IIV during childhood and adolescence and its application in the investigation of development of language acquisition, infant-parent interactions, development of motor skills, cognitive development, mood regulation, and identity development are examined in Part 2. Part 3 focuses on IIV during adult development, including its use in neuropsychological functioning and attention and in personality development and mood regulation. IIV in the context of adults’ health behavior is also reviewed. Part 4 examines the key issues and challenges of IIV research in human development such as whether IIV in adult development is an indicator of vulnerability or resilience, the association between short-term IIV and long-term developmental change, and multiple time-scale design and analysis. The volume concludes with a look at the future of intraindividual variation analysis. Intended for advanced students and researchers in developmental psychology across the life-span, social, personality, and health psychology, as well as sociology, family studies, gerontology, education, and medicine, interested in intraindividual variability of behavior and its role in human development, this book also serves as a text for graduate courses on longitudinal analysis, multilevel modeling, and/or (advanced) data analysis offered in these departments. Knowledge in human development or life course sociology and graduate-level statistics is recommended.
Bayesian Statistical Modeling With Stan R And Python
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Author : Kentaro Matsuura
language : en
Publisher: Springer Nature
Release Date : 2023-01-24
Bayesian Statistical Modeling With Stan R And Python written by Kentaro Matsuura and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-24 with Computers categories.
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.
Models For Intensive Longitudinal Data
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Author : Theodore A. Walls
language : en
Publisher: Oxford University Press
Release Date : 2006-01-19
Models For Intensive Longitudinal Data written by Theodore A. Walls and has been published by Oxford University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-01-19 with Mathematics categories.
Rapid technological advances in devices used for data collection have led to the emergence of a new class of longitudinal data: intensive longitudinal data (ILD). Behavioral scientific studies now frequently utilize handheld computers, beepers, web interfaces, and other technological tools for collecting many more data points over time than previously possible. Other protocols, such as those used in fMRI and monitoring of public safety, also produce ILD, hence the statistical models in this volume are applicable to a range of data. The volume features state-of-the-art statistical modeling strategies developed by leading statisticians and methodologists working on ILD in conjunction with behavioral scientists. Chapters present applications from across the behavioral and health sciences, including coverage of substantive topics such as stress, smoking cessation, alcohol use, traffic patterns, educational performance and intimacy. Models for Intensive Longitudinal Data (MILD) is designed for those who want to learn about advanced statistical models for intensive longitudinal data and for those with an interest in selecting and applying a given model. The chapters highlight issues of general concern in modeling these kinds of data, such as a focus on regulatory systems, issues of curve registration, variable frequency and spacing of measurements, complex multivariate patterns of change, and multiple independent series. The extraordinary breadth of coverage makes this an indispensable reference for principal investigators designing new studies that will introduce ILD, applied statisticians working on related models, and methodologists, graduate students, and applied analysts working in a range of fields. A companion Web site at www.oup.com/us/MILD contains program examples and documentation.
Smoothness Priors Analysis Of Time Series
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Author : Genshiro Kitagawa
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Smoothness Priors Analysis Of Time Series written by Genshiro Kitagawa 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.
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.
Instrument Engineers Handbook Volume Two
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Author : Bela G. Liptak
language : en
Publisher: CRC Press
Release Date : 2018-10-08
Instrument Engineers Handbook Volume Two written by Bela G. Liptak and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-10-08 with Technology & Engineering categories.
The latest update to Bela Liptak's acclaimed "bible" of instrument engineering is now available. Retaining the format that made the previous editions bestsellers in their own right, the fourth edition of Process Control and Optimization continues the tradition of providing quick and easy access to highly practical information. The authors are practicing engineers, not theoretical people from academia, and their from-the-trenches advice has been repeatedly tested in real-life applications. Expanded coverage includes descriptions of overseas manufacturer's products and concepts, model-based optimization in control theory, new major inventions and innovations in control valves, and a full chapter devoted to safety. With more than 2000 graphs, figures, and tables, this all-inclusive encyclopedic volume replaces an entire library with one authoritative reference. The fourth edition brings the content of the previous editions completely up to date, incorporates the developments of the last decade, and broadens the horizons of the work from an American to a global perspective. Béla G. Lipták speaks on Post-Oil Energy Technology on the AT&T Tech Channel.
The Mathematics Of Financial Modeling And Investment Management
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Author : Sergio M. Focardi
language : en
Publisher: John Wiley & Sons
Release Date : 2004-04-12
The Mathematics Of Financial Modeling And Investment Management written by Sergio M. Focardi 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 2004-04-12 with Business & Economics categories.
the mathematics of financial modeling & investment management The Mathematics of Financial Modeling & Investment Management covers a wide range of technical topics in mathematics and finance-enabling the investment management practitioner, researcher, or student to fully understand the process of financial decision-making and its economic foundations. This comprehensive resource will introduce you to key mathematical techniques-matrix algebra, calculus, ordinary differential equations, probability theory, stochastic calculus, time series analysis, optimization-as well as show you how these techniques are successfully implemented in the world of modern finance. Special emphasis is placed on the new mathematical tools that allow a deeper understanding of financial econometrics and financial economics. Recent advances in financial econometrics, such as tools for estimating and representing the tails of the distributions, the analysis of correlation phenomena, and dimensionality reduction through factor analysis and cointegration are discussed in depth. Using a wealth of real-world examples, Focardi and Fabozzi simultaneously show both the mathematical techniques and the areas in finance where these techniques are applied. They also cover a variety of useful financial applications, such as: * Arbitrage pricing * Interest rate modeling * Derivative pricing * Credit risk modeling * Equity and bond portfolio management * Risk management * And much more Filled with in-depth insight and expert advice, The Mathematics of Financial Modeling & Investment Management clearly ties together financial theory and mathematical techniques.