Regularization and predictor selection for ordinal and categorical data

Gertheiss, Jan; Tutz, Gerhard

doi:10.1007/978-3-031-31186-4_7

Regularization and predictor selection for ordinal and categorical data

Publication date

2023-04-07

Document type

Sammelbandbeitrag oder Buchkapitel

Author

Gertheiss, Jan

Tutz, Gerhard

Organisational unit

Statistik und Datenwissenschaften

DOI

10.1007/978-3-031-31186-4_7

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/20343

Publisher

Springer

Book title

Trends and challenges in categorical data analysis

ISBN

978-3-031-31186-4

First page

199

Last page

232

Peer-reviewed

✅

Part of the university bibliography

✅

Language

English

Abstract

Categorical data are quite common in applied statistics, and various regularized fitting procedures have been proposed for appropriate handling of such variables in regression. This chapter gives an overview of some of those techniques. Special emphasis is put on model selection with ordinal explanatory variables if the number of categories and/or the number of predictors is relatively large. In this context, regularized fitting is compared to more classical procedures like forward selection. Structuring of ordinal predictors is considered within the framework of generalized additive models (GAMs), and tools provided within this framework are employed for statistical inference.

Version

Published version

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