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Handbook of Discrete-Valued Time Series

Richard A. Davis Scott H. Holan Robert T. Lund Nalini Ravishanker

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CRC Press Inc
21 December 2015
Economic statistics; Probability & statistics
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.
Edited by:   Richard A. Davis, Scott H. Holan, Robert T. Lund, Nalini Ravishanker
Imprint:   CRC Press Inc
Country of Publication:   United States
Dimensions:   Height: 254mm,  Width: 178mm,  Spine: 30mm
Weight:   1.043kg
ISBN:   9781466577732
ISBN 10:   1466577738
Series:   Chapman & Hall/CRC Handbooks of Modern Statistical Methods
Pages:   464
Publication Date:   21 December 2015
Audience:   College/higher education ,  Professional and scholarly ,  Primary ,  Undergraduate
Format:   Hardback
Publisher's Status:   Active
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 Tjostheim 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 Corberan-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

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.

Reviews for Handbook of Discrete-Valued Time Series

This book is rather more specialized in its coverage of the modelling of different observed count-process-based time series and would be suitable for statistical researchers and graduate students. It is enhanced with a good number of interesting examples...Generally, the book includes theoretical derivations and formulae that have been written in a readily understood and simple way and it makes it easy for the reader to follow the corresponding applications...Overall, this is a good authoritative source. The authors have gathered material within specific topics to make it a useful and easy reference for researchers who are interested in count data time series. This book is aimed at postgraduate students and it can be used as a research source. -Safaa Kadhem, Plymouth University, Journal of the Royal Statistical Society, Series A, January 2017 The analysis of discrete-valued time series has generated much interest amongst time series analysts in recent years...This book is a very important contribution to the analysis of discrete-valued time series...The handbook will be a very valuable source for anyone who is interested in the analysis of integer-valued processes and will be a reference book for years to come. -Alain LaTour (Universite Grenoble Alpes, France), published in the Journal of Time Series Analysis ... this volume will be useful for researchers involved in the modeling time series of counts. However, some of the techniques available in the handbook can be implemented for other types of discrete-valued time series...The style and structure of the handbook is unified. All the articles are well structured and consistent in style and presentation...The book is a good mix of methodological and application chapters...Researchers and professionals looking to learn more in this field of study could benefit from articles showcased in this handbook. The content in most of these selected chapters has proved to be an enjoyable read. -Technometrics, July 2016


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