Identifying potential significant factors impacting zero-inflated proportions data

Ribaud, Melina; Gabriel, Edith; Hughes, Joseph; Soubeyrand, Samuel

Identifying potential significant factors impacting zero-inflated proportions data

Document detail

ID

oai:HAL:hal-02936779v4

Topic

Permutation Tests Spearman's correlation Performance Indicator Covid-19 Equine Influenza Ranking COVID-19 Equine Influenza Performa... COVID-19 Permutation Test [MATH.MATH-ST]Mathematics [math]/S... [SDV.EE.SANT]Life Sciences [q-bio]...

Author

Ribaud, Melina Gabriel, Edith Hughes, Joseph Soubeyrand, Samuel

Langue

Editor

HAL CCSD

Year

2023

listing date

12/6/2023

Keywords

permutation influenza factors zipd covid-19 data

Metrics

Abstract

Classical supervised methods like linear regression and decision trees are not completely adapted for identifying impacting factors on a response variable corresponding to zero-inflated proportion data (ZIPD) that are dependent, continuous and bounded.In this article we propose a within-block permutation-based methodology to identify factors (discrete or continuous) that are significantly correlated with ZIPD, we propose a performance indicator quantifying the percentage of correlation explained by the subset of significant factors, and we show how to predict the ranks of the response variables conditionally on the observation of these factors.The methodology is illustrated on simulated data and on two real data sets dealing with epidemiology.

In the first data set, ZIPD correspond to probabilities of transmission of Influenza between horses.

In the second data set, ZIPD correspond to probabilities that geographic entities (e.g., states and countries) have the same COVID-19 mortality dynamics.

Ribaud, Melina,Gabriel, Edith,Hughes, Joseph,Soubeyrand, Samuel, 2023, Identifying potential significant factors impacting zero-inflated proportions data, HAL CCSD