Prediction intervals for Poisson‐based regression models. (21st June 2021)

• Record Type: Journal Article
• Authors: Kim, Taeho; Lieberman, Benjamin; Luta, George; Peña, Edsel A.
• Is Part Of: Wiley interdisciplinary reviews. Volume 14:Number 5(2022)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-06-21
• Subjects: COVID‐19 -- over‐dispersion -- Poisson model -- Poisson regression -- prediction interval; Mathematical statistics -- Data processing -- Periodicals; Science -- Data processing -- Periodicals; Social sciences -- Data processing -- Periodicals; Mathematical statistics -- Periodicals; 519.50285
• DOI: 10.1002/wics.1568
• Languages: English
• ISSNs: 1939-5108

Abstract

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 Abstract : Review of prediction interval (PI) construction for Poisson regression is provided. Starting from the independent and identically distributed setting and then proceeding to the regression setting, the focus is on the PI construction for the Poisson and over‐dispersed models. Lastly, studies on PI construction pertinent to the COVID‐19 pandemic are presented.