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Handbook of Discrete-Valued Time Series
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Handbook of Discrete-Valued Time Series

Book Details

Format Paperback / Softback
ISBN-10 0367570394
ISBN-13 9780367570392
Publisher Taylor & Francis Ltd
Imprint Chapman & Hall/CRC
Country of Manufacture GB
Country of Publication GB
Publication Date Jun 30th, 2020
Print length 484 Pages
Weight 453 grams
Ksh 13,700.00
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This handbook 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. The book examines the advantages and limitations of the various modeling techniques and keeps probabilistic, technical details to a minimum. While

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.


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