Improvement of Kernel Type Density Estimators

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Abstract
Estimation of the value of a density function at a point of continuity using a kernel-type estimator is discussed and improvements of the technique are presented. The generalized jackknife method is employed to reduce the asymptotic and small sample mean square error and bias of these estimators. The procedure presented has the flexibility to afford the user a choice between bias reduction, variance reduction, or both.

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