@book{douc_nonlinear_2014,
	address = {Boca Raton},
	series = {Chapman \& {Hall}/{CRC} texts in statistical science series},
	title = {Nonlinear times series: theory, methods and applications with {R} examples},
	isbn = {978-1-4665-0225-3},
	shorttitle = {Nonlinear times series},
	abstract = {"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"--},
	publisher = {CRC Press, Taylor \& Francis Group, CRC Press is an imprint of the Taylor \& Francis Group, an Informa business},
	author = {Douc, Randal and Moulines, Eric and Stoffer, David S.},
	year = {2014},
	keywords = {MATHEMATICS / Probability \& Statistics / General, Mathematical models, Time-series analysis}
}