Synopsis
Rigorous, unified time series methods for stationary processes. This book offers a clear path through the theory and practice of analyzing stationary time series, emphasizing a mathematically precise yet intuition-friendly approach.
The text presents a rigorous treatment aimed at the theoretical statistician while also showing how these methods apply to physical sciences and engineering. It explains why many traditional techniques may fall short and how nonparametric ideas can handle large classes of processes, not just low-order models. Readers will find both proofs and practical comments that illuminate when and how the results can be used in real problems.
- A solid foundation for spectral analysis and the structure of stationary processes
- A shift from simple, low-order models to high-order, flexible approaches
- Practical guidance on estimation, testing, and interpretation in applications
- Realistic discussions of modeling choices in noise, turbulence, and related fields
Ideal for researchers and students in statistics, engineering, and the physical sciences who seek a rigorous yet applicable treatment of time series analysis.
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