Imperial College London
Portrait Picture

Emeritus ProfessorKarimAbadir

Business School

Emeritus Professor in Financial Econometrics
 
 
 
//

Contact

 

k.m.abadir

 
 
//

Location

 

3.0353 Prince's GateSouth Kensington Campus

//

Summary

 

Publications

Citation

BibTex format

@article{Abadir:2024:biomet/asae007,
author = {Abadir, K and Lubrano, M},
doi = {biomet/asae007},
journal = {Biometrika},
title = {Explicit solution for the asymptotically-optimal bandwidth in cross-validation},
url = {http://dx.doi.org/10.1093/biomet/asae007},
year = {2024}
}
Download

RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We show that least squares cross-validation methods share a common structure which has an explicit asymptotic solution, when the chosen kernel is asymptotically separable in bandwidth and data. For density estimation with a multivariate Student t(ν) kernel, the cross-validation criterion becomes asymptotically equivalent to a polynomial of only three terms. Our bandwidth formulae are simple and noniterative thus leading to very fast computations, their integrated squared-error dominates traditional cross-validation implementations, they alleviate the notorious sample variability of cross-validation, and overcome its breakdown in the case of repeated observations. We illustrate our method with univariate and bivariate applications, of density estimation and nonparametric regressions, to a large dataset of Michigan State University academic wages and experience.
AU  - Abadir,K
AU  - Lubrano,M
DO  - biomet/asae007
PY  - 2024///
SN  - 0006-3444
TI  - Explicit solution for the asymptotically-optimal bandwidth in cross-validation
T2  - Biometrika
UR  - http://dx.doi.org/10.1093/biomet/asae007
UR  - http://hdl.handle.net/10044/1/109660
ER  -
Download