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References (18)
-
Yunshyong Chow, S. Geman, L.-D. Wu (1983)
Consistent Cross-Validated Density EstimationAnnals of Statistics, 11
-
P. Hall (1982)
Cross-validation in density estimationBiometrika, 69
-
D. Scott, L. Factor (1981)
Monte Carlo Study of Three Data-Based Nonparametric Probability Density EstimatorsJournal of the American Statistical Association, 76
-
M. Stone (1976)
Cross‐Validatory Choice and Assessment of Statistical PredictionsJournal of the royal statistical society series b-methodological, 36
-
P. Hall (1983)
Large Sample Optimality of Least Squares Cross-Validation in Density EstimationAnnals of Statistics, 11
-
J. Aitchison, C. Aitken (1976)
Multivariate binary discrimination by the kernel methodBiometrika, 63
-
E. Schuster, G. Gregory (1981)
On the Nonconsistency of Maximum Likelihood Nonparametric Density Estimators -
G. Wahba, S. Wold (1975)
A completely automatic french curve: fitting spline functions by cross validationCommunications in Statistics-theory and Methods, 4
-
M. Stone (1974)
Cross-validation and multinomial predictionBiometrika, 61
-
M. Rudemo (1982)
Empirical Choice of Histograms and Kernel Density Estimators -
G. Wahba (1981)
Data-Based Optimal Smoothing of Orthogonal Series Density EstimatesAnnals of Statistics, 9
-
A. Bowman (1980)
A note on consistency of the kernel method for the analysis of categorical dataBiometrika, 67
-
D. Titterington (1980)
A Comparative Study of Kernel-Based Density Estimates for Categorical DataTechnometrics, 22
-
K. Davis (1981)
Estimation of the Scaling Parameter for a Kernel-Type Density EstimateJournal of the American Statistical Association, 76
-
M. Fryer (1976)
Some Errors Associated with the Non-parametric Estimation of Density FunctionsIma Journal of Applied Mathematics, 18
-
M. Fryer (1977)
A Review of Some Non-parametric Methods of Density EstimationIma Journal of Applied Mathematics, 20
-
R. Duin (1976)
On the Choice of Smoothing Parameters for Parzen Estimators of Probability Density FunctionsIEEE Transactions on Computers, C-25
-
D. Scott, R. Tapia, James Thompson (1977)
Kernel density estimation revisitedNonlinear Analysis-theory Methods & Applications, 1
- Publisher
- Oxford University Press
- Copyright
- © 1984 Biometrika Trust
- ISSN
- 0006-3444
- eISSN
- 1464-3510
- DOI
- 10.1093/biomet/71.2.353
- Publisher site
- See Article on Publisher Site
Abstract
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. This content is only available as a PDF. © 1984 Biometrika Trust
Journal
Biometrika – Oxford University Press
Published: Aug 1, 1984