psda: A tool for extracting knowledge from symbolic data with an application in Brazilian educational data

Silva, Wagner J. F.; Souza, Renata M. C. R. de; Cysneiros, Francisco José A.

doi:10.1007/s00500-020-05252-5

Cited by 3 publications

(1 citation statement)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As already mentioned, this paper applies its theoretical contributions to educational data. In the framework of SDA applications for the educational scenario, Silva et al [ 28 ] introduced a toolbox for symbolic polygonal data that was applied to data of the Brazilian Basic Education Assessment System (SAEB). The authors performed this application to estimate the mathematical proficiency of Brazilian students in the final year of elementary education using a symbolic regression model.…”

Section: Related Work For Interval Linear Regression Analysismentioning

confidence: 99%

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

Nascimento

Fagundes

Souza

et al. 2022

Pattern Anal Applic

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Related Work For Interval Linear Regression Analysismentioning

confidence: 99%