PART II ANALYSIS OF PERIODIC DATA AND MODEL SELECTION
by DeWayne Derryberry
Basic Data Analysis for Time Series with R
- PREFACE
- ACKNOWLEDGMENTS
- PART I BASIC CORRELATION STRUCTURES
- PART II ANALYSIS OF PERIODIC DATA AND MODEL SELECTION
- 7 Review of Transcendental Functions and Complex Numbers
- 8 The Power Spectrum and the Periodogram
- 9 Smoothers, The Bias-Variance Tradeoff, and the Smoothed Periodogram
- 10 A Regression Model for Periodic Data
- 11 Model Selection and Cross-Validation
- 12 Fitting Fourier series
- 13 Adjusting for AR(1) Correlation in Complex Models
- 13.1 Introduction
- 13.2 The Two-Sample t-Test—UNCUT and Patch-Cut Forest-Test—UNCUT and Patch-Cut Forest
- 13.3 The Second Sleuth Case—Global Warming, A Simple Regression
- 13.4 The Semmelweis Intervention
- 13.5 The NYC Temperatures (Adjusted)
- 13.6 The Boise River Flow Data: Model Selection With Filtering
- 13.7 Implications of AR(1) Adjustments and the “Skip” Method
- 13.8 Summary
- Exercises
- PART III COMPLEX TEMPORAL STRUCTURES
- 14 The backshift operator, the impulse response function, and general ARMA models
- 15 The Yule–Walker Equations and the Partial Autocorrelation Function
- 16 Modeling philosophy and Complete Examples
- 16.1 Modeling overview
- 16.2 A complex periodic model—Monthly river flows, Furnas 1931–1978
- 16.3 A modeling example—trend and periodicity: CO2 levels at Mauna Lau
- 16.4 Modeling periodicity with a possible intervention—two examples
- 16.5 Periodic models: monthly, weekly, and daily averages
- 16.6 Summary
- Exercises
- PART IV SOME DETAILED AND COMPLETE EXAMPLES
- REFERENCES
- INDEX
- End User License Agreement
-
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