Abstract #301788

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JSM 2003 Abstract #301788
Activity Number: 24
Type: Contributed
Date/Time: Sunday, August 3, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Nonparametric Statistics
Abstract - #301788
Title: The Analysis of Continuous Mixture Data in Dose-Response Studies Using Nonparametric Methods
Author(s): Fengjuan Xuan*+ and Thomas P. Hettmansperger
Companies: Pennsylvania State University and Pennsylvania State University
Address: 0326 Thomas Building, University Park, PA, 16802-2111,
Keywords: nonparametric mixture ; density estimator ; minimum disparity estimation ; robust
Abstract:

Measurements on a continuous variable in dose-response studies may indicate that not all animals are affected by the treatment. Boos and Brownie (1991) used a two-component normal mixture model to describe the treatment distribution. The problem is to estimate the proportion of the treatment group who respond to the treatment and to estimate the treatment effect. A comparable nonparametric mixture model is proposed where measurements in the treatment group arise from a mixture of (i) a component analogous to the distribution of the response measurement in the control group and (ii) a mean shifted component. Two nonparametric approaches are developed to solve this problem when the normal distribution is not assumed: (i) a nonparametric EM algorithm where the component density is estimated by a kernel type estimator and (ii) MDE (minimum disparity estimation). Robustness and efficiency are also discussed for both estimators.

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