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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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton
CRC Press, Taylor & Francis Group
[2014]
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| Series: | Texts in statistical science
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| Call Number : | QA 280 .D68 2014 |
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| 020 | |a 9781466502253 | ||
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| 090 | |a QA 280 |b .D68 2014 | ||
| 100 | 1 | |a Douc, Randal |c author | |
| 245 | 1 | 0 | |a Nonlinear times series |b theory, methods and applications with R examples |c Randal Douc, Eric Moulines, David S. Stoffer |
| 264 | 1 | |a Boca Raton |b CRC Press, Taylor & Francis Group |c [2014] | |
| 264 | 4 | |c ©2014 | |
| 300 | |a xx, 531 pages |b illustrations |c 24 cm | ||
| 336 | |a text |2 rdacontent | ||
| 337 | |a unmediated |2 rdamedia | ||
| 338 | |a volume |2 rdacarrier | ||
| 490 | 1 | |a Chapman & Hall/CRC texts in statistical science series | |
| 504 | |a Includes bibliographical references and index | ||
| 505 | 0 | |a 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 | |
| 520 | |a 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. | ||
| 592 | |a 32588 |b 5/12/16 |c RM348.24 |h Bookline | ||
| 700 | 1 | |a Moulines, Eric |e author | |
| 700 | 1 | |a Stoffer, David S. |e author | |
| 830 | 0 | |a Texts in statistical science | |
| 999 | |a vtls000057316 |c 102261 |d 102261 | ||