Bayesian analysis of time series. (2019)

• Record Type: Book
• Further Information: Note: Lyle D. Broemeling.
• Authors: Broemeling, Lyle D, 1939-
• Contents: Table of Contents 1. Introduction to the Bayesian Analysis of Time Series ; Introduction; Bayesian Analysis; Fundamentals of Time Series Analysis; Basic Random Models; Time Series and Regression; Time Series and Stationarity; Time Series and Spectral Analysis; Dynamic Linear Model; The Shift Point Problem; Residuals and Diagnostic Tests; References ; 2. Bayesian Analysis ; Introduction; Bayes’ Theorem; Prior Information; The Binomial Distribution; The Normal Distribution; Posterior Information; The Binomial Distribution; The Normal Distribution; The Poisson Distribution; Inference; Introduction; Estimation; Testing Hypotheses; Predictive Inference; Introduction; The Binomial Population; Forecasting from a Normal Population; Checking Model Assumptions; Introduction; Forecasting from an Exponential, but Assuming a Normal Population; A Poisson Population; The Wiener Process; Testing the Multinomial Assumption; Computing; Introduction; Monte Carlo Markov Chains; Introduction; The Metropolis Algorithm; Gibbs Sampling; The Common Mean of Normal Populations; An Example; Comments and Conclusions; Exercises; References 3. Preliminary Considerations for Time Series ; Time Series; Airline Passenger Bookings; Sunspot Data; Los Angeles Annual Rainfall; Graphical Techniques; Plot of Air Passenger Bookings; Sunspot Data; Graph of Los Angeles Rainfall Data; Trends, Seasonality, and Trajectories; Decomposition; Decompose Air Passenger Bookings; Average Monthly Temperatures for Debuque, Iowa; Graph of Los Angeles Rainfall Data; Mean, Variance, Correlation and General Sample Characteristic of a Time Series; Other Fundamental Considerations; Summary and Conclusions; Exercises; References 4. Basic Random Models ; Introduction; White Noise; A Random Walk; Another Example; Goodness of Fit; Predictive Distributions; Comments and Conclusions; Exercises; References 5. Time Series and Regression ; Introduction; Linear Models; Linear Regression with Seasonal Effects and Autoregressive Models; Bayesian Inference for a Non-Linear Trend in Time Series; Nonlinear Trend with Seasonal Effects; Regression with AR(2) Errors; Simple Linear Regression Model; Nonlinear Regression with Seasonal Effects; Comments and Conclusions; Exercises; References 6. Time Series and Stationarity; Moving Average Models; Regression Models with Moving Average Errors; Regression Model with MA Errors and Seasonal Effects; Autoregressive Moving Average Models; Another Approach for the Bayesian analysis of MA Processes; Second Order Moving Average Process; Quadratic Regression With MA(2) Residuals; Regression Model With MA(2) Errors and Seasonal Effects; Forecasting with Moving Average Processes; Another Example; Testing Hypotheses; Forecasting with a Moving Average Time Series; Exercises; References 7. Time Series and Spectral Analysis ; Introduction; The Fundamentals; Unit of Measurement of Frequency; The Spectrum; Examples; Bayesian Spectral Analysis of Autoregressive Moving Average Series; MA(1) Process; MA(2) Series; The AR(1) Time Series; AR(2); ARMA(1, 1) Time Series; Sunspot Cycle; Comments and Conclusions; Exercises; References 8. Dynamic Linear Models; Introduction; Discrete Time Linear Dynamic Systems; Estimation of the States; Filtering; Smoothing; Prediction; The Control problem; Example; The Kalman Filter; The Control Problem; Adaptive Estimation; An Example of Adaptive Estimation; Testing Hypotheses; Summary; Exercises; References 9. The Shift Point Problem in Time Series ; Introduction; A Shifting Normal Sequence; Structural Change in an Autoregressive Time Series One Shift in a MA(1) Time Series; Changing Models in Econometrics; Regression Model with Autocorrelated Errors; Another Example of Structural Change; Testing Hypotheses; Analyzing Threshold Autoregression with the Bayesian Approach; A Numerical Example of Threshold Autoregression; Comments and Conclusions; Exercises; References 10. Residuals and Diagnostic Tests; Introduction; Diagnostic Checks for Autoregressive Models; Residuals for Model of Color Data; Residuals and Diagnostic Checks for Regression Models with AR(1) Errors; Diagnostic Tests for Regression Models with Moving Average Time Series; Comments and Conclusions; Exercises; References
• Edition: 1st
• Publisher Details: Boca Raton : Chapman & Hall/CRC
• Publication Date: 2019
• Extent: 1 online resource, illustrations (black and white)
• Subjects: 519.55; Time-series analysis; Bayesian statistical decision theory
• Languages: English
• ISBNs: 9780429948916; 9780429948923; 9780429948909; 9780429488443
• Related ISBNs: 9781138591523
• Notes: Note: Includes bibliographical references.; Note: Description based on CIP data; resource not viewed.
• Access Rights: Legal Deposit; Only available on premises controlled by the deposit library and to one user at any one time; The Legal Deposit Libraries (Non-Print Works) Regulations (UK).
• Access Usage: Restricted: Printing from this resource is governed by The Legal Deposit Libraries (Non-Print Works) Regulations (UK) and UK copyright law currently in force.