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