Handbook of discrete-valued time series
Author(s)
Bibliographic Information
Handbook of discrete-valued time series
（Handbooks of modern statistical methods / Series editors, Garrett Fitzmaurice）
CRC Press, 2020, c2016
- : pbk
Available at 2 libraries
Note
Other editors: Scott H. Holan, Robert Lund, Nalini Ravishanker
Includes bibliographical references and index
Description and Table of Contents
Description
Model a Wide Range of Count Time Series
Handbook of Discrete-Valued Time Series presents state-of-the-art methods for modeling time series of counts and incorporates frequentist and Bayesian approaches for discrete-valued spatio-temporal data and multivariate data. While the book focuses on time series of counts, some of the techniques discussed can be applied to other types of discrete-valued time series, such as binary-valued or categorical time series.
Explore a Balanced Treatment of Frequentist and Bayesian Perspectives
Accessible to graduate-level students who have taken an elementary class in statistical time series analysis, the book begins with the history and current methods for modeling and analyzing univariate count series. It next discusses diagnostics and applications before proceeding to binary and categorical time series. The book then provides a guide to modern methods for discrete-valued spatio-temporal data, illustrating how far modern applications have evolved from their roots. The book ends with a focus on multivariate and long-memory count series.
Get Guidance from Masters in the Field
Written by a cohesive group of distinguished contributors, this handbook provides a unified account of the diverse techniques available for observation- and parameter-driven models. It covers likelihood and approximate likelihood methods, estimating equations, simulation methods, and a Bayesian approach for model fitting.
Table of Contents
Methods for Univariate Count Processes. Diagnostics and Applications. Binary and Categorical-Valued Time Series. Discrete-Valued Spatio-Temporal Processes. Multivariate and Long Memory Discrete-Valued Processes.
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