PENAKSIRAN FUNGSI DENSITAS UNTUK SUATU DATA DENGAN PENAKSIR KERNEL
Keywords: kernel function, oversmoothing, cross validation
Abstract
One of the estimating of density function which has been recognized is histogram. Histogram has some weaknesses, i.e. the different starting points and the width of class intervals. Different starting points or different class intervals result different histogram forms. This article is about the estimating of the density function by using kernel function. This method does not require the determination starting points and the interval class width. The obtained curve has a smooth density function, a small sampling variance, and the important information from data are still kept.
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References
Becker, R. A., Chamber, J.M. 1988. The New S Language. Bell Telephone Laboratories, Inc., Murray Hill, New Yersey.
Elan Computer Group . 1993. S-plus User’s Manual Version 3.2. Math. Soft. Inc., Seattle.
Hardle,W. 1991. Smoothing Techniques. Springer-Verlag, New York.
Scott, D.W. 1992. Multivariate Density Estimation. John Wiley, New York.
Silverman,B.W. 1986. Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.
Hogg, R. V., and Klugman, S. A. 1984. Loss Distributions. Library of Congress Cataloging in Publications Data.
Elan Computer Group . 1993. S-plus User’s Manual Version 3.2. Math. Soft. Inc., Seattle.
Hardle,W. 1991. Smoothing Techniques. Springer-Verlag, New York.
Scott, D.W. 1992. Multivariate Density Estimation. John Wiley, New York.
Silverman,B.W. 1986. Density Estimation for Statistics and Data Analysis. Chapman and Hall, London.
Hogg, R. V., and Klugman, S. A. 1984. Loss Distributions. Library of Congress Cataloging in Publications Data.
Published
Aug 15, 2003
DOI:
How to Cite
Sunandi, N. ., & Malau, R. A. . (2003). PENAKSIRAN FUNGSI DENSITAS UNTUK SUATU DATA DENGAN PENAKSIR KERNEL. Jurnal Matematika Sains Dan Teknologi, 4(1), 24–34. https://doi.org/10.33830/jmst.v4i1.666.2003
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