Semiparametric Bayesian Inference for Time Series with Mixed Spectra PDF Download
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Author: Christopher K. Carter
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
A Bayesian analysis is presented of a time series which is the sum of a stationary component with a smooth spectral density and a deterministic component consisting of a linear combination of a trend and periodic terms. The periodic terms may have known or unknown frequencies. The advantage of our approach is that different features of the data such as the regression parameters, the spectral density, unknown frequencies, and missing observations are combined in a hierarchical Bayesian framework and estimated simultaneously. A Bayesian test to detect the presence of deterministic components in the data is also constructed. By using an asymptotic approximation to the likelihood, the computation is carried out efficiently using Markov chain Monte Carlo in O(Mn) operations, where n is the sample size and M and is the number of iterations. We show empirically that our approach works well on real and simulated examples.
Author: Christopher K. Carter
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
A Bayesian analysis is presented of a time series which is the sum of a stationary component with a smooth spectral density and a deterministic component consisting of a linear combination of a trend and periodic terms. The periodic terms may have known or unknown frequencies. The advantage of our approach is that different features of the data such as the regression parameters, the spectral density, unknown frequencies, and missing observations are combined in a hierarchical Bayesian framework and estimated simultaneously. A Bayesian test to detect the presence of deterministic components in the data is also constructed. By using an asymptotic approximation to the likelihood, the computation is carried out efficiently using Markov chain Monte Carlo in O(Mn) operations, where n is the sample size and M and is the number of iterations. We show empirically that our approach works well on real and simulated examples.
Author: C. K. Carter
Publisher:
ISBN: 9781862742406
Category : Time-series analysis
Languages : en
Pages : 30
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Book Description
Author: Ta-Hsin Li
Publisher: CRC Press
ISBN: 1420010069
Category : Mathematics
Languages : en
Pages : 648
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Book Description
Time series with mixed spectra are characterized by hidden periodic components buried in random noise. Despite strong interest in the statistical and signal processing communities, no book offers a comprehensive and up-to-date treatment of the subject. Filling this void, Time Series with Mixed Spectra focuses on the methods and theory for the stati
Author: Raquel Prado
Publisher: CRC Press
ISBN: 1420093363
Category : Mathematics
Languages : en
Pages : 375
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Book Description
Focusing on Bayesian approaches and computations using simulation-based methods for inference, Time Series: Modeling, Computation, and Inference integrates mainstream approaches for time series modeling with significant recent developments in methodology and applications of time series analysis. It encompasses a graduate-level account of Bayesian time series modeling and analysis, a broad range of references to state-of-the-art approaches to univariate and multivariate time series analysis, and emerging topics at research frontiers. The book presents overviews of several classes of models and related methodology for inference, statistical computation for model fitting and assessment, and forecasting. The authors also explore the connections between time- and frequency-domain approaches and develop various models and analyses using Bayesian tools, such as Markov chain Monte Carlo (MCMC) and sequential Monte Carlo (SMC) methods. They illustrate the models and methods with examples and case studies from a variety of fields, including signal processing, biomedicine, and finance. Data sets, R and MATLAB® code, and other material are available on the authors’ websites. Along with core models and methods, this text offers sophisticated tools for analyzing challenging time series problems. It also demonstrates the growth of time series analysis into new application areas.
Author:
Publisher: Elsevier
ISBN: 0080461174
Category : Mathematics
Languages : en
Pages : 1062
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Book Description
This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics
Author: Paul Damien
Publisher: OUP Oxford
ISBN: 0191647004
Category : Mathematics
Languages : en
Pages : 720
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Book Description
The development of hierarchical models and Markov chain Monte Carlo (MCMC) techniques forms one of the most profound advances in Bayesian analysis since the 1970s and provides the basis for advances in virtually all areas of applied and theoretical Bayesian statistics. This volume guides the reader along a statistical journey that begins with the basic structure of Bayesian theory, and then provides details on most of the past and present advances in this field. The book has a unique format. There is an explanatory chapter devoted to each conceptual advance followed by journal-style chapters that provide applications or further advances on the concept. Thus, the volume is both a textbook and a compendium of papers covering a vast range of topics. It is appropriate for a well-informed novice interested in understanding the basic approach, methods and recent applications. Because of its advanced chapters and recent work, it is also appropriate for a more mature reader interested in recent applications and developments, and who may be looking for ideas that could spawn new research. Hence, the audience for this unique book would likely include academicians/practitioners, and could likely be required reading for undergraduate and graduate students in statistics, medicine, engineering, scientific computation, business, psychology, bio-informatics, computational physics, graphical models, neural networks, geosciences, and public policy. The book honours the contributions of Sir Adrian F. M. Smith, one of the seminal Bayesian researchers, with his papers on hierarchical models, sequential Monte Carlo, and Markov chain Monte Carlo and his mentoring of numerous graduate students -the chapters are authored by prominent statisticians influenced by him. Bayesian Theory and Applications should serve the dual purpose of a reference book, and a textbook in Bayesian Statistics.
Author: John Geweke
Publisher: Oxford University Press
ISBN: 0191618268
Category : Business & Economics
Languages : en
Pages : 576
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Book Description
Bayesian econometric methods have enjoyed an increase in popularity in recent years. Econometricians, empirical economists, and policymakers are increasingly making use of Bayesian methods. This handbook is a single source for researchers and policymakers wanting to learn about Bayesian methods in specialized fields, and for graduate students seeking to make the final step from textbook learning to the research frontier. It contains contributions by leading Bayesians on the latest developments in their specific fields of expertise. The volume provides broad coverage of the application of Bayesian econometrics in the major fields of economics and related disciplines, including macroeconomics, microeconomics, finance, and marketing. It reviews the state of the art in Bayesian econometric methodology, with chapters on posterior simulation and Markov chain Monte Carlo methods, Bayesian nonparametric techniques, and the specialized tools used by Bayesian time series econometricians such as state space models and particle filtering. It also includes chapters on Bayesian principles and methodology.
Author: Andrew C. Harvey
Publisher: Oxford University Press on Demand
ISBN: 0199278695
Category : Business & Economics
Languages : en
Pages : 475
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Book Description
This volume presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with them. The book is intended to give a self-contained presentation of the methods and applicative issues. Harvey has made major contributions to this field and provides substantial introductions throughout the book to form a unified view of the literature. - ;This book presents a collection of readings which give the reader an idea of the nature and scope of unobserved components (UC) models and the methods used to deal with th.
Author: Subhashis Ghosal
Publisher: Cambridge University Press
ISBN: 0521878268
Category : Business & Economics
Languages : en
Pages : 671
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Book Description
Bayesian nonparametrics comes of age with this landmark text synthesizing theory, methodology and computation.
Author: Siddhartha Chib
Publisher: Emerald Group Publishing
ISBN: 1848553099
Category : Business & Economics
Languages : en
Pages : 672
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Book Description
Illustrates the scope and diversity of modern applications, reviews advances, and highlights many desirable aspects of inference and computations. This work presents an historical overview that describes key contributions to development and makes predictions for future directions.
