Lyle D. Broemeling

Bayesian Analysis of Time Series

• Broschiertes Buch

Produktbeschreibung

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 awide 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.

Produktdetails

• Verlag: Chapman and Hall/CRC / Taylor & Francis
• Seitenzahl: 294
• Erscheinungstermin: 31. März 2021
• Englisch
• Abmessung: 234mm x 156mm x 16mm
• Gewicht: 453g
• ISBN-13: 9780367779993
• ISBN-10: 0367779994
• Artikelnr.: 61212366

Autorenporträt

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.

Inhaltsangabe

1. Introduction. 2. Bayesian Inference : The prior, posterior and predictive distributions. 3. Plot Trends , Seasonal Variation and Decomposition of a Series. 4. Autocorrelation, Partial Correlation, and Cross Correlation. 5. Bayesian Data Analysis for Some Fundamental Time Series. 6. Bayesian Regression Analysis with Time Series Errors. 7. Bayesian Methods for Stationary Models 8. An Analysis for Non-Stationary Models. 9. Bayesian Spectrum Analysis. 10. System Identification from a Bayesian Perspective. 11. Multivariate Models. 12. Dynamic Linear Models for Time Series. 13. Bayesian Posterior Distributions for Non-Linear Models.14. Bilinear Models and Threshold Autoregressive Processes. 15. Miscellaneous Topics in Time Series.

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1. Introduction. 2. Bayesian Inference : The prior, posterior and
predictive distributions. 3. Plot Trends , Seasonal Variation and
Decomposition of a Series. 4. Autocorrelation, Partial Correlation, and
Cross Correlation. 5. Bayesian Data Analysis for Some Fundamental Time
Series. 6. Bayesian Regression Analysis with Time Series Errors. 7.
Bayesian Methods for Stationary Models 8. An Analysis for Non-Stationary
Models. 9. Bayesian Spectrum Analysis. 10. System Identification from a
Bayesian Perspective. 11. Multivariate Models. 12. Dynamic Linear Models
for Time Series. 13. Bayesian Posterior Distributions for Non-Linear
Models.14. Bilinear Models and Threshold Autoregressive Processes. 15.
Miscellaneous Topics in Time Series.

Rezensionen

"...(This book) by Lyle D. Broemeling is an excellent source to learn time series concepts, methods, expressions, and interpretations from the Bayesian viewpoint using R code and WinBugs code...The book is suitable for usage to teach in a graduate-level Bayesian time series course...The references are exhaustive and well selected for the readers. The exercises are challenging."
- Ramalingam Shanmugam, JSCS, Aug 2020