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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Yes, you can access Nonlinear Time Series by Randal Douc, Eric Moulines, David Stoffer in PDF and/or ePUB format, as well as other popular books in Mathematics & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Publisher

Chapman and Hall/CRC

Year

2014

ISBN

9781466502345

Edition

Topic

Mathematics

Subtopic

Probability & Statistics

Index

Mathematics

Front Cover
Contents
Preface
Frequently Used Notation
Part I: Foundations
Part II: Markovian Models
Part III: State Space and Hidden Markov Models
Part IV: Appendices
References