Bootstrapping generalized linear models to accommodate overdispersed count data

Katherine Burak () and Adam Kashlak
Additional contact information
Katherine Burak: University of British Columbia
Adam Kashlak: University of Alberta

Statistical Papers, 2024, vol. 65, issue 6, No 16, 3769-3788

Abstract: Abstract When modelling counts or rates using Poisson regression, it is common to find overdispersion in data. Overdispersed count data is prevalent in a variety of applied research areas such as ecology and finance when the variance of the response is higher than the Poisson distribution allows. While there are models that are capable of handling data of this nature, conducting inference when presented with overdispersed data poses some challenges. Classical parametric approaches to inference may fail to be reliable when computing bounds for confidence regions as the mean-variance assumption of the Poisson distribution may not be satisfied. Bootstrap approaches are a viable alternative and we explore the performance of the one-step residual and wild bootstrap as a means to perform inference for regression parameters. Furthermore, we adopt an analytic approach to bootstrapping that is able to accommodate overdispersion, while being preferable from an efficiency perspective.

Keywords: Concentration inequalities; Poisson; Rademacher; Resampling (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s00362-024-01534-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:stpapr:v:65:y:2024:i:6:d:10.1007_s00362-024-01534-4

Ordering information: This journal article can be ordered from
http://www.springer. ... business/journal/362

DOI: 10.1007/s00362-024-01534-4

Access Statistics for this article

Statistical Papers is currently edited by C. Müller, W. Krämer and W.G. Müller

More articles in Statistical Papers from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().