Bagging cross-validated bandwidths with application to Big Data
In: Bagging cross-validated bandwidths with application to Big Data. Biometrika (2021), 108(4), 981-988; (2024)
Online
report
Hall and Robinson (2009) proposed and analyzed the use of bagged cross-validation to choose the bandwidth of a kernel density estimator. They established that bagging greatly reduces the noise inherent in ordinary cross-validation, and hence leads to a more efficient bandwidth selector. The asymptotic theory of Hall and Robinson (2009) assumes that $N$, the number of bagged subsamples, is $\infty$. We expand upon their theoretical results by allowing $N$ to be finite, as it is in practice. Our results indicate an important difference in the rate of convergence of the bagged cross-validation bandwidth for the cases $N=\infty$ and $N<\infty$. Simulations quantify the improvement in statistical efficiency and computational speed that can result from using bagged cross-validation as opposed to a binned implementation of ordinary cross-validation. The performance of thebagged bandwidth is also illustrated on a real, very large, data set. Finally, a byproduct of our study is the correction of errors appearing in the Hall and Robinson (2009) expression for the asymptotic mean squared error of the bagging selector.
Comment: 37 pages, 9 figures
Titel: |
Bagging cross-validated bandwidths with application to Big Data
|
---|---|
Autor/in / Beteiligte Person: | Barreiro-Ures, Daniel ; Cao, Ricardo ; Fernández, Mario Francisco ; Hart, Jeffrey D. |
Link: | |
Quelle: | Bagging cross-validated bandwidths with application to Big Data. Biometrika (2021), 108(4), 981-988; (2024) |
Veröffentlichung: | 2024 |
Medientyp: | report |
DOI: | 10.1093/biomet/asaa092 |
Schlagwort: |
|
Sonstiges: |
|