Open Access
2023 Spline local basis methods for nonparametric density estimation
J. Lars Kirkby, Álvaro Leitao, Duy Nguyen
Author Affiliations +
Statist. Surv. 17: 75-118 (2023). DOI: 10.1214/23-SS142


This work reviews the literature on spline local basis methods for non-parametric density estimation. Particular attention is paid to B-spline density estimators which have experienced recent advances in both theory and methodology. These estimators occupy a very interesting space in statistics, which lies aptly at the cross-section of numerous statistical frameworks. New insights, experiments, and analyses are presented to cast the various estimation concepts in a unified context, while parallels and contrasts are drawn to the more familiar contexts of kernel density estimation. Unlike kernel density estimation, the study of local basis estimation is not yet fully mature, and this work also aims to highlight the gaps in existing literature which merit further investigation.

Funding Statement

Á. Leitao wishes to acknowledge the support received from the CITIC research centre, funded by Xunta de Galicia and the European Union (European Regional Development Fund, Galicia 2014-2020 Program) by grant ED431G 2019/01.


Download Citation

J. Lars Kirkby. Álvaro Leitao. Duy Nguyen. "Spline local basis methods for nonparametric density estimation." Statist. Surv. 17 75 - 118, 2023.


Received: 1 August 2022; Published: 2023
First available in Project Euclid: 3 April 2023

MathSciNet: MR4569645
zbMATH: 07690329
Digital Object Identifier: 10.1214/23-SS142

Vol.17 • 2023
Back to Top