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Nonlinear times series theory, methods and applications with R examples

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 app...

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Bibliographic Details
Main Authors: Douc, Randal author, Moulines, Eric (Author), Stoffer, David S. (Author)
Format: Book
Language:English
Published: Boca Raton CRC Press, Taylor & Francis Group [2014]
Series:Texts in statistical science
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Table of Contents:
  • Linear Models
  • Linear Gaussian State Space Models
  • Beyond Linear Models
  • Stochastic Recurrence Equations
  • Markov Models: Construction and Definitions
  • Stability and Convergence
  • Sample Paths and Limit Theorems
  • Inference for Markovian Models
  • Non-Gaussian and Nonlinear State Space Models
  • Particle Filtering
  • Particle Smoothing
  • Inference for Nonlinear State Space Models
  • Asymptotics of the MLE for NLSS

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