Description
Book SynopsisUniquely combining theory, application, and computing, this book explores the spectral approach to time series analysis The use of periodically correlated (or cyclostationary) processes has become increasingly popular in a range of research areas such as meteorology, climate, communications, economics, and machine diagnostics.
Trade Review"
Periodically Correlated Random Sequences is an ideal text on time series analysis for graduate-level statistics and engineering students who have previous experience in second-order stochastic processes (Hilbert space), vector spaces, random processes, and probability. This book also serves as a valuable reference for research statisticians and practioners in areas of probability and statistics such as time series analysis, stochastic processes, and prediction theory." (
Mathematical Review, Issue 2009e)
Table of ContentsPreface xiii
Acknowledgments xv
Glossary xvii
1 Introduction
2 Examples, Models, and Simulations 19
3 Review of Hilbert Spaces 45
4 Stationary Random Sequences 67
5 Harmonizable Sequence 133
6 Fourier Theory of the Covariance 151
7 Representations of PC Sequences 199
8 Prediction of Sequences 215
9 Estimation of Mean and Covariance 249
10 Spectral Estimation 297
11 A Paradigm for Nonparametric Analysis of PC Time Series 331
References 337
Index 351
