This book offers a useful combination of probabilistic and statistical tools for analyzing
nonlinear time series. Key features of the book include a study of the extremal behavior of
nonlinear time series and a comprehensive list of nonlinear models that address different
aspects of nonlinearity. Several inferential methods including quasi likelihood methods
sequential Markov Chain Monte Carlo Methods and particle filters are also included so as to
provide an overall view of the available tools for parameter estimation for nonlinear models. A
chapter on integer time series models based on several thinning operations which brings
together all recent advances made in this area is also included. Readers should have attended
a prior course on linear time series and a good grasp of simulation-based inferential methods
is recommended. This book offers a valuable resource for second-year graduate students and
researchers in statistics and other scientific areas who need a basic understanding of
nonlinear time series.
