Nonlinear Time Series: Theory, Methods and Applications with R Examples

✍ Scribed by Randal Douc, Eric Moulines, David Stoffer

Publisher: Chapman and Hall/CRC
Year: 2014
Tongue: English
Leaves: 555
Series: Chapman & Hall/CRC Texts in Statistical Science
Edition: 1
Category: Library

No coin nor oath required. For personal study only.

✦ Synopsis

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.

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