The goals of this book are to develop an appreciation for the richness and versatility of modern time series analysis as a tool for analyzing data, and still maintain a commitment to theoretical integrity, as exemplified by the seminal works of Brillinger (1981) and Hannan (1970) and the texts by Brockwell and Davis (1991) and Fuller (1995). The advent of more powerful computing, esยญ pecially in the last three years, has provided both real data and new software that can take one considerably beyond the fitting ofยทsimple time domain modยญ els, such as have been elegantly described in the landmark work of Box and Jenkins (1970). The present book is designed to be useful as a text for courses in time series on several different levels and as a reference work for practitionยญ ers facing the analysis of time-correlated data in the physical, biological, and social sciences. We believe the book will be useful as a text at both the undergraduate and graduate levels. An undergraduate course can be accessible to students with a background in regression analysis and might include Sections 1. 1-1. 8, 2. 1-2. 9, and 3. 1-3. 8. Similar courses have been taught at the University of California (Berkeley and Davis) in the past using the earlier book on applied time series analysis by Shumway (1988). Such a course is taken by undergraduate students in mathematics, economics, and statistics and attracts graduate students from the agricultural, biological, and environmental sciences
CONTENT
1: Characteristics of Time Series -- 2: Time Series Regression and ARIMA Models -- 3: Spectral Analysis and Filtering -- 4: State-Space and Multivariate ARMAX Models -- 5: Statistical Methods in the Frequency Domain -- References
Statistics
Statistics
Statistical Theory and Methods
Statistics for Engineering Physics Computer Science Chemistry and Earth Sciences
Statistics for Life Sciences Medicine Health Sciences
Statistics for Social Science Behavorial Science Education Public Policy and Law
