Davis / Holan / Lund Handbook of Discrete-Valued Time Series

1. Auflage 2016
ISBN: 978-1-4665-7774-9
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 484 Seiten

Reihe: Chapman & Hall/CRC Handbooks of Modern Statistical Methods

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.

Autoren/Hrsg.

Davis, Richard A.

Holan, Scott H.

Lund, Robert

Ravishanker, Nalini

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik

Inhaltsverzeichnis

Methods for Univariate Count Processes
Statistical Analysis of Count Time Series Models: AGLM Perspective
Konstantinos Fokianos
Markov Models for Count Time Series
Harry Joe
Generalized Linear Autoregressive Moving Average Models
William T.M. Dunsmuir
Count Time Series with Observation-Driven Autoregressive Parameter Dynamics
Dag Tjøstheim
Renewal-Based Count Time Series
Robert Lund and James Livsey
State Space Models for Count Time Series
Richard A. Davis and William T.M. Dunsmuir
Estimating Equation Approaches for Integer-Valued Time Series Models
Aerambamoorthy Thavaneswaran and Nalini Ravishanker
Dynamic Bayesian Models for Discrete-Valued Time Series
Dani Gamerman, Carlos A. Abanto-Valle, Ralph S. Silva, and Thiago G. Martins

Diagnostics and Applications
Model Validation and Diagnostics
Robert C. Jung, Brendan P.M. McCabe, and A.R. Tremayne
Detection of Change Points in Discrete-Valued Time Series
Claudia Kirch and Joseph Tadjuidje Kamgaing
Bayesian Modeling of Time Series of Counts with Business Applications
Refik Soyer, Tevfik Aktekin, and Bumsoo Kim

Binary and Categorical-Valued Time Series
Hidden Markov Models for Discrete-Valued Time Series
Iain L. MacDonald and Walter Zucchini
Spectral Analysis of Qualitative Time Series
David Stoffer
Coherence Consideration in Binary Time Series Analysis
Benjamin Kedem

Discrete-Valued Spatio-Temporal Processes
Hierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data
Scott H. Holan and Christopher K. Wikle
Hierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued Data
Christopher K. Wikle and Mevin B. Hooten
Autologistic Regression Models for Spatio-Temporal Binary Data
Jun Zhu and Yanbing Zheng
Spatio-Temporal Modeling for Small Area Health Analysis
Andrew B. Lawson and Ana Corberán-Vallet

Multivariate and Long Memory Discrete-Valued Processes
Models for Multivariate Count Time Series
Dynamic Models for Time Series of Counts with a Marketing Application
Nalini Ravishanker, Rajkumar Venkatesan, and Shan Hu
Long Memory Discrete-Valued Time Series
Robert Lund, Scott H. Holan, and James Livsey

Über Autor(innen)

Richard A. Davis is the chair and Howard Levene Professor of Statistics at Columbia University. He is also president (2015–2016) of the Institute of Mathematical Statistics. In 1998, he won (with collaborator W.T.M. Dunsmuir) the Koopmans Prize for Econometric Theory. His research interests include time series, applied probability, extreme value theory, and spatial-temporal modeling. He received his PhD in mathematics from the University of California, San Diego.

Scott H. Holan is a professor in the Department of Statistics at the University of Missouri. He is a fellow of the American Statistical Association and an elected member of the International Statistics Institute. His research primarily focuses on time series analysis, spatial-temporal methodology, Bayesian methods, and hierarchical models and is largely motivated by problems in federal statistics, econometrics, ecology, and environmental science. He received his PhD in statistics from Texas A&M University.

Robert Lund is a professor in the Department of Mathematical Sciences at Clemson University. He is a fellow of the American Statistical Association and was the 2005–2007 chief editor of the reviews section of the Journal of the American Statistical Association. His research interests include time series, applied probability, and statistical climatology. He received his PhD in statistics from the University of North Carolina.

Nalini Ravishanker is a professor in the Department of Statistics at the University of Connecticut. She is a fellow of the American Statistical Association and elected member of the International Statistical Institute, the theory and methods editor of Applied Stochastic Models in Business and Industry, and an associate editor for the Journal of Forecasting. Her research interests include time series, times-to-events modeling, and Bayesian dynamic modeling, with applications to ecology, marketing, and transportation engineering. She received her PhD in statistics and operations research from the Stern School of Business, New York University.