This book will describe how to use models that explain the probabilistic characteristics of a time series while the Bayesian approach will provide inferences about those probabilistic characteristics.
This book will describe how to use models that explain the probabilistic characteristics of a time series while the Bayesian approach will provide inferences about those probabilistic characteristics.
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.
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.
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.
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
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