Prediction intervals for Poisson-based regression models

Taeho Kim, Benjamin Lieberman, George Luta, Edsel A. Peña*

*Corresponding author for this work
4 Citations (Scopus)

Abstract

This paper provides a review of the literature regarding methods for constructing prediction intervals for counting variables, with particular focus on those whose distributions are Poisson or derived from Poisson and with an over-dispersion property. Independent and identically distributed models and regression models are both considered. The motivating problem for this review is that of predicting the number of daily and cumulative cases or deaths attributable to COVID-19 at a future date. This article is categorized under: Applications of Computational Statistics > Clinical Trials Statistical Learning and Exploratory Methods of the Data Sciences > Modeling Methods Statistical Models > Generalized Linear Models.

Original languageEnglish
Article numbere1568
JournalWiley Interdisciplinary Reviews: Computational Statistics
Volume14
Issue number5
ISSN1939-5108
DOIs
Publication statusPublished - Sept 2022

Keywords

  • COVID-19
  • over-dispersion
  • Poisson model
  • Poisson regression
  • prediction interval

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