Online Program Home
  My Program

All Times EDT

Abstract Details

Activity Number: 243 - Statistics in Sports and Beyond
Type: Contributed
Date/Time: Wednesday, August 11, 2021 : 10:00 AM to 11:50 AM
Sponsor: International Statistical Institute
Abstract #317762
Title: A Flexible Univariate Moving Average Time-Series Model for Dispersed Count Data
Author(s): Kimberly Sellers* and Ali Arab and Sean Melville and Fanyu Cui
Companies: Georgetown University / U.S. Census Bureau and Georgetown University and Citigroup and Yimian by Ascential
Keywords: over-dispersion; under-dispersion; Conway-Maxwell-Poisson (COM-Poisson or CMP); sum-of-Conway-Maxwell-Poisson (sCMP)

While the Poisson moving average (PMA) model is a popular approach to describe the relation among integer-valued time series data, this model is constrained by the underlying equi-dispersion assumption (i.e., that the variance and the mean equal). This work instead introduces a flexible integer-valued moving average model for count data that contain over- or under-dispersion via the Conway-Maxwell-Poisson (CMP) distribution and related distributions. This first-order sum-of-Conway-Maxwell-Poissons moving average (SCMPMA(1)) model offers a generalizable construct that includes the PMA (among others) as a special case. We highlight the SCMPMA model properties and illustrate its flexibility via simulated and real data examples.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2021 program