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On Multiplicative Bias Correction in Kernel Density Estimation
M. C. Jones, D. F. Signorini and N. L. Hjort
Sankhyā: The Indian Journal of Statistics, Series A (1961-2002)
Vol. 61, No. 3 (Oct., 1999), pp. 422-430
Published by: Indian Statistical Institute
Stable URL: http://www.jstor.org/stable/25051272
Page Count: 9
You can always find the topics here!Topics: Density estimation, Estimators, Estimation bias, Parametric models, Estimators for the mean, Estimation methods, Simulations, Vehicle performance, Sample size, Wands
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Hjort and Glad (1995) present a method for semiparametric density estimation. Relative to the ordinary kernel density estimator, this technique performs much better when a parametric vehicle distribution fits the data, and otherwise performs at broadly the same level. Jones, Linton and Nielsen (1995) present a somewhat similar method for density estimation which has higher order bias for all sufficiently smooth densities. In this paper, we combine the two methods. We show that, theoretically, the desired properties of general higher order bias allied with even better performance for an appropriate vehicle model are achieved. Simulations suggest that the new estimator realises only a little of its theoretical potential in practice for small to moderately large sample sizes.
Sankhyā: The Indian Journal of Statistics, Series A (1961-2002) © 1999 Indian Statistical Institute