Handbook of Discrete-Valued Time Series: Handbooks of Modern Statistical Methods

ISBN-13: 9780367570392 / Angielski / Miękka / 2020 / 484 str.

Richard A. Davis; Scott H. Holan; Robert Lund

Opis

This handbook 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. The book examines the advantages and limitations of the various modeling techniques and keeps probabilistic, technical details to a minimum. While

Spis treści

Methods for Univariate Count Processes. Diagnostics and Applications. Binary and Categorical-Valued Time Series. Discrete-Valued Spatio-Temporal Processes. Multivariate and Long Memory Discrete-Valued Processes.

O autorze

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.