Nonlinear Time Series Analysis

The developments of particular filters (sequential Monte Carlos methods) and hidden Markov models have substantially increased the theory and applications of nonlinear statistical methods. Time series analysis is no exception. In addition, advances in computing and data collection have made available large data sets and high-frequency data. For example, hourly data of air pollutants are widely collected at various monitoring stations throughout the world. These new data make it not only feasible, but also necessary to take into consideration the nonlinearity embedded in most real-world time series. On the other hand, only a small number of books available deal with the topic of nonlinear time series analysis. Dealing with theory and applications, this book considers both parametric and nonparametric methods, nonlinear state-space models, and Bayesian as well as classical approaches to nonlinear time series analysis. The book discusses the advantages and limitations of the nonlinear models and methods as well as improvements upon linear time series models.  The book will be of interest to students, scholars and practitioners in statistics and any field with time series applications.