Spline local basis methods for nonparametric density estimation
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Spline local basis methods for nonparametric density estimationDate
2023Citation
J. Lars Kirkby, Álvaro Leitao, and Duy Nguyen, "Spline local basis methods for nonparametric density estimation", Statist. Surv. 17, 75 - 118, 2023. https://doi.org/10.1214/23-SS142
Abstract
[Abstract]: 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.
Keywords
Wavelet Estimator
Wavelets
Minimax Risk
density estimation
Wavelets
Minimax Risk
density estimation
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Atribución 4.0 Internacional BY