English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

This paper presents the application of a new semi-analytical method of linearregression for Poisson count data to COVID-19 events. The regression is basedon the Bonamente and Spence (2022) maximum-likelihood solution for the best-fitparameters, and this paper introduces a simple analytical solution for thecovariance matrix that completes the problem of linear regression with Poissondata. The analytical nature for both parameter estimates and their covariancematrix is made possible by a convenient factorization of the linear modelproposed by J. Scargle (2013). The method makes use of the asymptoticproperties of the Fisher information matrix, whose inverse provides thecovariance matrix. The combination of simple analytical methods to obtain boththe maximum-likelihood estimates of the parameters, and their covariancematrix, constitute a new and convenient method for the linear regression ofPoisson-distributed count data, which are of common occurrence across a varietyof fields. A comparison between this new linear regression method and twoalternative methods often used for the regression of count data -- the ordinaryleast-square regression and the $\chi^2$ regression -- is provided with theapplication of these methods to the analysis of recent COVID-19 count data. Thepaper also discusses the relative advantages and disadvantages among thesemethods for the linear regression of Poisson count data.

Linear regression for Poisson count data: A new semi-analytical method with applications to COVID-19 events