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Handbook of Discrete-Valued Time Series

Handbooks of Modern Statistical Methods

Edited by: Richard A. Davis , Scott H. Holan , Robert Lund , Nalini Ravishanker

Print publication date:  December  2015
Online publication date:  January  2016

Print ISBN: 9781466577732
eBook ISBN: 9781466577749
Adobe ISBN:

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Book 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

Chapter  1:  Statistical Analysis of Count Time Series Models: A GLM Perspective Download PDF
Chapter  2:  Markov Models for Count Time Series Download PDF
Chapter  3:  Generalized Linear Autoregressive Moving Average Models Download PDF
Chapter  4:  Count Time Series with Observation-Driven Autoregressive Parameter Dynamics Download PDF
Chapter  5:  Renewal-Based Count Time Series Download PDF
Chapter  6:  State Space Models for Count Time Series Download PDF
Chapter  7:  Estimating Equation Approaches for Integer-Valued Time Series Models Download PDF
Chapter  8:  Dynamic Bayesian Models for Discrete-Valued Time Series Download PDF
Chapter  9:  Model Validation and Diagnostics Download PDF
Chapter  10:  Detection of Change Points in Discrete-Valued Time Series Download PDF
Chapter  11:  Bayesian Modeling of Time Series of Counts with Business Applications Download PDF
Chapter  12:  Hidden Markov Models for Discrete-Valued Time Series Download PDF
Chapter  13:  Spectral Analysis of Qualitative Time Series Download PDF
Chapter  14:  Coherence Consideration in Binary Time Series Analysis Download PDF
Chapter  15:  Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data Download PDF
Chapter  16:  Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data Download PDF
Chapter  17:  Autologistic Regression Models for Spatio-Temporal Binary Data Download PDF
Chapter  18:  Spatio-Temporal Modeling for Small Area Health Analysis Download PDF
Chapter  19:  Models for Multivariate Count Time Series Download PDF
Chapter  20:  Dynamic Models for Time Series of Counts with a Marketing Application Download PDF
Chapter  21:  Long Memory Discrete-Valued Time Series Download PDF
prelims Download PDF
Index Download PDF
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