Nonlinear Time Series: Theory, Methods and Applications with R Examples
Book information
Description
Features Describes the major statistical techniques for inferring model parameters, with a focus on the MLE and QMLE Introduces concepts of nonparametric statistics, including smoothing splines Covers HMM models, including Gaussian linear, switching Markovian, and nonlinear state space models Present direct likelihood inference techniques and the EM algorithm Uses R for numerical examples and provides a dedicated R package Solutions manual available upon qualifying course adoption 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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