Nonlinear times series : theory, methods and applications with R examples
Material type:
TextSeries: Texts in statistical sciencePublication details: Boca Raton : CRC Press Taylor & Francis Group, 2014Description: xx, 531 p. ; 24 cmISBN: 9781466502253 Subject(s): Time-series analysis | Mathematical models | Probability & StatisticsDDC classification: 519.55 Summary: "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"--
| Item type | Current library | Collection | Call number | Status | Date due | Barcode |
|---|---|---|---|---|---|---|
Reference
|
Bangalore University Library Reference | Statistics | 519.55 DOU (Browse shelf (Opens below)) | Available | 351591 |
Browsing Bangalore University Library shelves, Shelving location: Reference, Collection: Statistics Close shelf browser (Hides shelf browser)
| 519.542 SUN Statistical Modelling By Exponential Families | 519.544 EGG Maximum penalized likelihood estimation : | 519.546 TAT Survival Analysis | 519.55 DOU Nonlinear times series : | 570.151 95 DAG Introductory applied biostatistics | 610.727 JON K Design and analysis of cross-over trials | 610.727 KLE Handbook of survival analysis |
Includes bibliographical references and index.
"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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