With a focus on the description, modeling, prediction, and signal extraction of times series, this book provides basic tools for analyzing time series that arise in real-world problems. It employs the state-space model as a generic tool for time series modeling and presents convenient recursive filtering and smoothing methods for the state-space models. The author takes a unified approach to model evaluation based on the entropy maximization principle advocated by Dr. Akaike. He also covers standard stationary time series models, nonstationary time series models, and simulation methods.
• Chapman & Hall/CRC Monographs on Statistics & Applied Probability
Statistical Theory & Methods
Statistics
Reference
Professional