Statistical inference for ordinal predictors in generalized additive models with application to Bronchopulmonary Dysplasia

Statistical inference for ordinal predictors in generalized additive models with application to Bronchopulmonary Dysplasia

Abstract Objective Discrete but ordered covariates are quite common in applied statistics, and some regularized fitting procedures have been proposed for proper handling of ordinal predictors in statistical models. Motivated by a study from neonatal medicine on Bronchopulmonary Dysplasia (BPD), we s...

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Bibliographic Details
Main Authors: Jan Gertheiss, Fabian Scheipl, Tina Lauer, Harald Ehrhardt
Format: Article
Language:English
Published: BMC 2022-03-01
Series:BMC Research Notes
Subjects:
Chronic lung disease
Logit model
Ordinal data
Regularization
Smoothing penalty
Online Access:https://doi.org/10.1186/s13104-022-05995-4

Internet

https://doi.org/10.1186/s13104-022-05995-4

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