PENAKSIRAN FUNGSI DENSITAS TIPE KERNEL DENGAN METODE CROSS-VALIDATION (C-V)
Keywords:
optimal bandwidth, Maximum Likelihood Cross-Validation, Unbiased Cross-Validation, Biased Cross-Validation
Abstract
The choice of the bandwidth h is the main problem of kernel density function. In some situations it might be quite useful to have a set of retes corresponding to different bandwidth.It is necessary to agree which bandwidth is an appropriate one. Cross-Validation (C-V) is a well-known method to optimize the smoothing parameter h. In this research, we will analysis about three methods of Cross-Validation: Maximum Likelihood Cross-Validation, Least-Square Cross-Validation (Unbiased Cross-Validation) and Biased Cross-Validation with bandwidth choice optimal.
Downloads
Download data is not yet available.
References
Apostol, T.M.(1967), Calculus Volume I, Wiley International Edition.
Devroye and Penrod,C.S, (1984), The Consistency of Kernel density Estimates, Journal The Annalisis Statistics,Vol 12.,No..4, 1231-1249
Everitt,Brian S. (1994), A Handbook of Statistical analyses using S-Plus, New York.Chapman&Hall
Gaser.T,Muller.H.G. Mamitcsch Society, Kernel for Nonparametric Curve Estimation, Journal of The Royal Statistic Society, Seri B, 47 236-252
Hardle,W (1990), Smoothing Tecnique With Implementation in S., Spinger Verlag, New York.
Hill, P.D. (1985), Kernel Estimation of Distribution Finction, Comm, Statist.Theory Methods, 14, 605-620
Jhon Rice (1984), Bandwidth Choice For Non Parametric Regression, Journal The Anall. Of Statistics, Volume 12, N0.4, 1215-1230
Jeffrey D. Hart and Thomas E. Wehrly, (1992), Kernel regression When The Boundary region in Large, With an Application to testing the Adequacy of Polinomial Medels, Journal of The American Statistical Assosiation, Vol.87.No.420. 1018-1024
Lethold,L (1976), The Calculus With Analitic Geometry, New York Harper International Edition.
Devroye and Penrod,C.S, (1984), The Consistency of Kernel density Estimates, Journal The Annalisis Statistics,Vol 12.,No..4, 1231-1249
Everitt,Brian S. (1994), A Handbook of Statistical analyses using S-Plus, New York.Chapman&Hall
Gaser.T,Muller.H.G. Mamitcsch Society, Kernel for Nonparametric Curve Estimation, Journal of The Royal Statistic Society, Seri B, 47 236-252
Hardle,W (1990), Smoothing Tecnique With Implementation in S., Spinger Verlag, New York.
Hill, P.D. (1985), Kernel Estimation of Distribution Finction, Comm, Statist.Theory Methods, 14, 605-620
Jhon Rice (1984), Bandwidth Choice For Non Parametric Regression, Journal The Anall. Of Statistics, Volume 12, N0.4, 1215-1230
Jeffrey D. Hart and Thomas E. Wehrly, (1992), Kernel regression When The Boundary region in Large, With an Application to testing the Adequacy of Polinomial Medels, Journal of The American Statistical Assosiation, Vol.87.No.420. 1018-1024
Lethold,L (1976), The Calculus With Analitic Geometry, New York Harper International Edition.
Published
2007-03-01
How to Cite
[1]
M. Talakua, “PENAKSIRAN FUNGSI DENSITAS TIPE KERNEL DENGAN METODE CROSS-VALIDATION (C-V)”, BAREKENG: J. Math. & App., vol. 1, no. 1, pp. 27-31, Mar. 2007.
Section
Articles
Authors who publish with this Journal agree to the following terms:
- Author retain copyright and grant the journal right of first publication with the work simultaneously licensed under a creative commons attribution license that allow others to share the work within an acknowledgement of the work’s authorship and initial publication of this journal.
- Authors are able to enter into separate, additional contractual arrangement for the non-exclusive distribution of the journal’s published version of the work (e.g. acknowledgement of its initial publication in this journal).
- Authors are permitted and encouraged to post their work online (e.g. in institutional repositories or on their websites) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published works.
