Discrete-valued time series are common in practice, but methods for their analysis are not well-known. In recent years, methods have been developed which are specifically designed for the analysis of discrete-valued time series. Hidden Markov and Other Models for Discrete-Valued Time Series introduces a new, versatile, and computationally tractable class of models, the "hidden Markov" models. It presents a detailed account of these models, then applies them to data from a wide range of diverse subject areas, including medicine, climatology, and geophysics. This book will be invaluable to researchers and postgraduate and senior undergraduate students in statistics. Researchers and applied statisticians who analyze time series data in medicine, animal behavior, hydrology, and sociology will also find this information useful.
An Introduction Using R, Second Edition Walter Zucchini, Iain L. MacDonald, Roland Langrock ... Hierarchical Modeling and Analysis for Spatial Data Sudipto Banerjee, Bradley P. Carlin, and Alan E. Gelfand (2004) 102.
The book provides a broad understanding of the models and their uses. After presenting the basic model formulation, the book covers estimation, forecasting, decoding, prediction, model selection, and Bayesian inference for HMMs.
... other models', though undoubtedly of theoretical interest, have led to few published applications. This is in marked contrast to hidden Markov models, which are of course applicable to more than just discrete-valued time series. These ...
... Other Models for Discrete–valued Time Series. Chapman & Hall, London, U.K. McCullagh, P. and Nelder, J. A. (1989). Generalized Linear Models 2nd edn. Chapman & Hall, London ... modeling framework 26 Handbook of Discrete-Valued Time Series.
This book seeks to rectify that state of affairs by providing a much needed introduction to discrete-valued time series, with particular focus on count-data time series. The main focus of this book is on modeling.
30, 397–430 (2002) Nze, P.A., Doukhan, P.:Weak dependence: models and applications. In: Dehling, H., Mikosch, T., Sørensen, M. (eds.) Empirical Process Techniques for Dependent Data, pp. 117–136. Birkhäuser, Boston (2002) Radulovic, ...
In Selected Proceedings of the Sheffield Symposium on Applied Probability , pp . 118–126 ( Eds . I. V. Basawa and R. L. Taylor ) . IMS Lecture Notes Monograph Ser . , Vol . 18. Hayward , CA. Johnson , N. L. , S. Kotz and A. W. Kemp ...
Time Series: Theory and Methods, second ed. Springer, New York. ... Time Series: Applications to Finance with R, third ed. Wiley, New York. ... Introductory Time Series with R. Springer Science+Business Media, LLC, New York.
Time Series: Theory and Methods, second ed. Springer, New York. ... Time Series: Applications to Finance with R, third ed. Wiley, New York. ... Introductory Time Series with R. Springer Science+Business Media, LLC, New York.
... hidden markov models for temporal data mining. In: Temporal data mining workshop Proceedings of KDD-2001, San Franciso, California, USA 12.15 Lin, W. Q., Orgun, M.A., Williams, G ... Time-series similarity problem and 286 Weiqiang Lin et al.