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An alternative method of cross-validation for the smoothing of density estimates
Abstract
Cross-validation with Kullback-Leibler loss function has been applied to the choice of a smoothing parameter in the kernel method of density estimation. A framework for this problem is constructed and used to derive an alternative method of cross-validation, based on integrated squared error, recently also proposed by Rudemo (1982). Hall (1983) has established the consistency and asymptotic optimality of the new method. For small and moderate sized samples, the performances of the two methods of cross-validation are compared on simulated data and specific examples.
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Citations
Cited by
A survey of cross-validation procedures for model selection
Arlot, S., & Celisse, A. (2010) · Statistics Surveys · doi:10.1214/09-ss054 · Cited by 3,522
BURMAN, P. (1989) · Biometrika · doi:10.1093/biomet/76.3.503 · Cited by 634
ks: Kernel Density Estimation and Kernel Discriminant Analysis for Multivariate Data in R
Duong, T. (2007) · Journal of Statistical Software · doi:10.18637/jss.v021.i07 · Cited by 593
How Far are Automatically Chosen Regression Smoothing Parameters from their Optimum?
Härdle, W., Hall, P., & Marron, J. S. (1988) · Journal of the American Statistical Association · doi:10.1080/01621459.1988.10478568 · Cited by 401
Cross-validation Bandwidth Matrices for Multivariate Kernel Density Estimation
DUONG, T., & HAZELTON, M. L. (2005) · Scandinavian Journal of Statistics · doi:10.1111/j.1467-9469.2005.00445.x · Cited by 317
Metadata
- Authors
- BOWMAN, A. W.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1984
- Volume
- 71
- Issue
- 2
- Pages
- 353-360
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W1998378660
- Cited by
- 934
- References
- 0
- FWCI
- 7.42
- Type
- journal article
- Language
- en
- Open access
- closed
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