This text emphasizes nonlinear models for a course in time series analysis. After introducing stochastic processes, Markov chains, Poisson processes, and ARMA models, the authors cover functional autoregressive, ARCH, threshold AR, and discrete time series models as well as several complementary approaches. They discuss the main limit theorems for Markov chains, useful inequalities, statistical techniques to infer model parameters, and GLMs. Moving on to HMM models, the book examines filtering and smoothing, parametric and nonparametric inference, advanced particle filtering, and numerical methods for inference.
- ISBN13 9781466502345
- Publish Date 6 January 2014 (first published 27 December 2013)
- Publish Status Active
- Publish Country US
- Publisher Taylor & Francis Ltd
- Imprint Chapman & Hall/CRC
- Format eBook
- Pages 551
- Language English