Nonparametric inference for interval data using kernel methods

Document Type

Article

Publication Date

1-1-2023

Abstract

Symbolic data have become increasingly popular in the era of big data. In this paper, we consider density estimation and regression for interval-valued data, a special type of symbolic data, common in astronomy and official statistics. We propose kernel estimators with adaptive bandwidths to account for variability of each interval. Specifically, we derive cross-validation bandwidth selectors for density estimation and extend the Nadaraya–Watson estimator for regression with interval data. We assess the performance of the proposed methods in comparison with existing kernel methods by extensive simulation studies and real data analysis.

Identifier

85145501864 (Scopus)

Publication Title

Journal of Nonparametric Statistics

External Full Text Location

https://doi.org/10.1080/10485252.2022.2160980

e-ISSN

10290311

ISSN

10485252

First Page

455

Last Page

473

Issue

3

Volume

35

Grant

2017R1A2B2012816

Fund Ref

Ministry of Science, ICT and Future Planning

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