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Activity Number: 205
Type: Contributed
Date/Time: Monday, July 30, 2007 : 2:00 PM to 3:50 PM
Sponsor: Section on Statistical Computing
Abstract - #309058
Title: Nonparametric Transformation of the Data to Obtain Bias Reduction in Kernel Estimation of the Distribution Function of Nonstandard Mixtures
Author(s): Ennis McCune*+ and Sandra L. McCune
Companies: Stephen F. Austin State University and Stephen F. Austin State University
Address: Box 13040 SFA, Nacogdoches, TX, 75962,
Keywords: kernel distribution function estimation ; nonstandard mixtures ; bias reduction
Abstract:

Nonstandard mixtures occur when a random variable behaves in a continuous manner except at a countable number of discrete mass points. Polansky (2005) introduced a biased kernel estimator of the distribution function of nonstandard mixtures. In this paper, a new estimator of the distribution function of nonstandard mixtures with less bias than Polansky's estimator is obtained by applying to Polansky's estimator a nonparametric data transformation bias-reduction technique introduced by Swanepoel and Van Graan (2005). Statistical properties of the new estimator are determined and presented.

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Revised September, 2007