"This is an excellent and very timely book dedicated to statistical methods developed for analysis of discrete-valued time series. The field of time series analysis has been mainly focused on continuous models. In general, however, these models are not appropriate for time series with discrete values (e.g., counts, binary, and categorical data). This book puts together the state of- the-art methods that are developed in recent years to address this issue….
[T]his current edition is … a quite impressive book addressing a very challenging problem, and it should be considered the starting point for all researchers interested in this topic."
―Babak Shahbaba, University of California, Irvine, in Journal of the American Statistical Association, January 2018
"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
"This is an excellent and very timely book dedicated to statistical methods developed for analysis of discrete-valued time series. The field of time series analysis has been mainly focused on continuous models. In general, however, these models are not appropriate for time series with discrete values (e.g., counts, binary, and categorical data). This book puts together the state of- the-art methods that are developed in recent years to address this issue….
[T]his current edition is … a quite impressive book addressing a very challenging problem, and it should be considered the starting point for all researchers interested in this topic."
―Babak Shahbaba, University of California, Irvine, in Journal of the American Statistical Association, January 2018
"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 (Université 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
From the Publisher
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.
About the Author
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.
