Missing Data Analysis in Regression

Many of the datasets in real-world applications contain incompleteness. In this paper, we approach the effects and possible solutions to incomplete databases in regression, aiming to bridge a gap between theoretically effective algorithms. We investigated the actual effects of missing data for regre...

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Bibliographic Details
Main Authors: C. G. Marcelino, G. M. C. Leite, P. Celes, C. E. Pedreira
Format: Article
Language:English
Published: Taylor & Francis Group 2022-12-01
Series:Applied Artificial Intelligence
Online Access:http://dx.doi.org/10.1080/08839514.2022.2032925