Nonparametric regression and classification with functional, categorical, and mixed covariates

Selk, Leonie; Gertheiss, Jan

doi:10.1007/s11634-022-00513-7

Nonparametric regression and classification with functional, categorical, and mixed covariates

Publication date

2022-09-02

Document type

Forschungsartikel

Author

Selk, Leonie

Gertheiss, Jan

Organisational unit

Statistik und Datenwissenschaften

DOI

10.1007/s11634-022-00513-7

URI

https://openhsu.ub.hsu-hh.de/handle/10.24405/18453

Publisher

Springer

Series or journal

Advances in data analysis and classification

ISSN

1862-5355

Periodical volume

17

Periodical issue

2

First page

519

Last page

543

Peer-reviewed

✅

Part of the university bibliography

✅

Language

English

Abstract

We consider nonparametric prediction with multiple covariates, in particular categorical or functional predictors, or a mixture of both. The method proposed bases on an extension of the Nadaraya-Watson estimator where a kernel function is applied on a linear combination of distance measures each calculated on single covariates, with weights being estimated from the training data. The dependent variable can be categorical (binary or multi-class) or continuous, thus we consider both classification and regression problems. The methodology presented is illustrated and evaluated on artificial and real world data. Particularly it is observed that prediction accuracy can be increased, and irrelevant, noise variables can be identified/removed by ‘downgrading’ the corresponding distance measures in a completely data-driven way.

Description

This article is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).

Version

Published version

Access right on openHSU

Metadata only access

Open Access Funding

Springer Nature (DEAL)