Handbook of Discrete-Valued Time Series

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.

Methods for Univariate Count ProcessesStatistical Analysis of Count Time Series Models: AGLM Perspective Konstantinos FokianosMarkov Models for Count Time Series Harry JoeGeneralized Linear Autoregressive Moving Average Models William T.M. DunsmuirCount Time Series with Observation-Driven Autoregressive Parameter Dynamics Dag TjøstheimRenewal-Based Count Time Series Robert Lund and James LivseyState Space Models for Count Time Series Richard A. Davis and William T.M. DunsmuirEstimating Equation Approaches for Integer-Valued Time Series ModelsAerambamoorthy Thavaneswaran and Nalini RavishankerDynamic Bayesian Models for Discrete-Valued Time Series Dani Gamerman, Carlos A. Abanto-Valle, Ralph S. Silva, and Thiago G. Martins Diagnostics and ApplicationsModel Validation and Diagnostics Robert C. Jung, Brendan P.M. McCabe, and A.R. TremayneDetection of Change Points in Discrete-Valued Time Series Claudia Kirch and Joseph Tadjuidje KamgaingBayesian Modeling of Time Series of Counts with Business Applications Refik Soyer, Tevfik Aktekin, and Bumsoo Kim Binary and Categorical-Valued Time SeriesHidden Markov Models for Discrete-Valued Time Series Iain L. MacDonald and Walter ZucchiniSpectral Analysis of Qualitative Time Series David StofferCoherence Consideration in Binary Time Series Analysis Benjamin Kedem Discrete-Valued Spatio-Temporal ProcessesHierarchical Dynamic Generalized Linear Mixed Models for Discrete-Valued Spatio-Temporal Data Scott H. Holan and Christopher K. WikleHierarchical Agent-Based Spatio-Temporal Dynamic Models for Discrete-Valued DataChristopher K. Wikle and Mevin B. HootenAutologistic Regression Models for Spatio-Temporal Binary DataJun Zhu and Yanbing ZhengSpatio-Temporal Modeling for Small Area Health AnalysisAndrew B. Lawson and Ana Corberán-Vallet Multivariate and Long Memory Discrete-Valued ProcessesModels for Multivariate Count Time Series Dynamic Models for Time Series of Counts with a Marketing ApplicationNalini Ravishanker, Rajkumar Venkatesan, and Shan HuLong Memory Discrete-Valued Time SeriesRobert 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.