The extension of quantile regression to count data raises several issues. We compare the traditional approach, based on transforming the count variable using jittering, with a recently proposed approach in which the coefficients of quantile regression are modelled by parametric functions. We exploit both methods to analyse university students’ data to evaluate the effect of emergency remote teaching due to COVID-19 on the number of credits earned by the students. The coefficients modelling approach performs a smoothing that is especially convenient in the tails of the distribution, preventing abrupt changes in the point estimates and increasing precision. Nonetheless, model selection is challenging because of the wide range of options and the limited availability of diagnostic tools. Thus the jittering approach remains fundamental to guide the choice of the parametric functions.

Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching / Carcaiso, Viviana; Grilli, Leonardo. - In: STATISTICAL METHODS & APPLICATIONS. - ISSN 1618-2510. - STAMPA. - ---:(2022), pp. 0-0. [10.1007/s10260-022-00661-2]

Quantile regression for count data: jittering versus regression coefficients modelling in the analysis of credits earned by university students after remote teaching

Grilli, Leonardo
2022

Abstract

The extension of quantile regression to count data raises several issues. We compare the traditional approach, based on transforming the count variable using jittering, with a recently proposed approach in which the coefficients of quantile regression are modelled by parametric functions. We exploit both methods to analyse university students’ data to evaluate the effect of emergency remote teaching due to COVID-19 on the number of credits earned by the students. The coefficients modelling approach performs a smoothing that is especially convenient in the tails of the distribution, preventing abrupt changes in the point estimates and increasing precision. Nonetheless, model selection is challenging because of the wide range of options and the limited availability of diagnostic tools. Thus the jittering approach remains fundamental to guide the choice of the parametric functions.
2022
---
0
0
Goal 4: Quality education
Carcaiso, Viviana; Grilli, Leonardo
File in questo prodotto:
File Dimensione Formato  
Carcaiso Grilli 2022 Quantile regression count data - SMA.pdf

accesso aperto

Descrizione: articolo
Tipologia: Pdf editoriale (Version of record)
Licenza: Open Access
Dimensione 1.37 MB
Formato Adobe PDF
1.37 MB Adobe PDF

I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1283599
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 0
  • ???jsp.display-item.citation.isi??? 0
social impact