The extension of quantile regression to count data raises several issues. In this research we compare a solution which exploits jittering to obtain a continuous working variable to a more recent approach, in which the coefficients of quantile regression are modeled by parametric functions. Both methods are applied for evaluating the effect of remote teaching on university students’ productivity. In this context, the latter approach is found to be advantageous.

Quantile regression coefficient modeling for counts to evaluate the productivity of university students / Viviana Carcaiso; Leonardo Grilli. - ELETTRONICO. - (2022), pp. 1333-1338. ((Intervento presentato al convegno 51st Scientific Meeting of the Italian Statistical Society tenutosi a Caserta nel 22-24 June 2022.

Quantile regression coefficient modeling for counts to evaluate the productivity of university students

Leonardo Grilli
2022

Abstract

The extension of quantile regression to count data raises several issues. In this research we compare a solution which exploits jittering to obtain a continuous working variable to a more recent approach, in which the coefficients of quantile regression are modeled by parametric functions. Both methods are applied for evaluating the effect of remote teaching on university students’ productivity. In this context, the latter approach is found to be advantageous.
SIS 2022 | Book of Short Papers
51st Scientific Meeting of the Italian Statistical Society
Caserta
22-24 June 2022
Viviana Carcaiso; Leonardo Grilli
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2158/1283140
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