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18.07.2022 | Theoretical Advances

Interval regression model adequacy checking and its application to estimate school dropout in Brazilian municipality educational scenario

verfasst von: Rafaella L. S. do Nascimento, Roberta A. de A. Fagundes, Renata M. C. R. de Souza, Francisco José A. Cysneiros

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2023

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Abstract

Interval-valued data have been commonly encountered in practice, and Symbolic Data Analysis provides a solution to the statistical treatment of these data. Regression analysis for interval-valued symbolic data is a topic that has been widely investigated in the literature of symbolic data analysis, and several models from different paradigms have been proposed. There are basic regression assumptions, and it is essential to validate them. This paper introduces an approach to check interval regression model adequacy based on residual analysis. Concepts of ordinary and standardized interval residual are presented, and graphical analysis of these residuals is also proposed. To show the usefulness of the proposed approach, an application for estimating school dropout in the scenario of Brazilian municipalities is performed. We observed some outliers from the interval residuals analysis, and interval robust regression models are more suitable for estimating school dropout.

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Titel: Interval regression model adequacy checking and its application to estimate school dropout in Brazilian municipality educational scenario
verfasst von: Rafaella L. S. do Nascimento
Roberta A. de A. Fagundes
Renata M. C. R. de Souza
Francisco José A. Cysneiros
Publikationsdatum: 18.07.2022
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 1/2023
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-022-01093-0

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