Randal Douc, Eric Moulines, David Stoffer

Nonlinear Time Series

Theory, Methods and Applications with R Examples

• Gebundenes Buch

Produktbeschreibung

"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"--

Produktdetails

• Chapman & Hall/CRC Texts in Statistical Science
• Verlag: Taylor & Francis Inc
• Seitenzahl: 552
• Erscheinungstermin: 6. Januar 2014
• Englisch
• Abmessung: 241mm x 159mm x 43mm
• Gewicht: 994g
• ISBN-13: 9781466502253
• ISBN-10: 1466502258
• Artikelnr.: 37328181

Autorenporträt

Randal Douc, Eric Moulines, David Stoffer

Inhaltsangabe

Preliminaries. Markov and Iterative Models: Nonlinear Markovian Models.
Stability, Recurrence, Mixing. Ergodicity, Limit Theorems. Parametric
Inference. Nonparametric Inference. Hidden Markov Models: Some HMM Models.
Filtering and Smoothing in HMM. Parametric Inference for HMM. Nonparametric
Inference for HMM. Particle Filtering Basics. Advanced Issues in Particle
Filtering. Particle Smoothing Basics. Numerical Methods for Inference.