CONDITIONAL RESAMPLING FOR MISCLASSIFIED MULTINOMIAL DATA WITH APPLICATIONS TO SAMPLING INSPECTION

被引:15
作者
HAITOVSKY, Y
RAPP, J
机构
[1] HEBREW UNIV JERUSALEM,DEPT STAT,IL-91905 JERUSALEM,ISRAEL
[2] HEBREW UNIV JERUSALEM,DEPT PSYCHOL,IL-91905 JERUSALEM,ISRAEL
关键词
CLASSIFICATION; DOUBLING SAMPLING; MAXIMUM LIKELIHOOD ESTIMATOR; MINIMIZE COST; MINIMUM VARIANCE; QUALITY CONTROL;
D O I
10.2307/1268946
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
A conditional resampling methodology is proposed for improving the estimators of multinomial classification probabilities in the presence of a fallible classifier. The (unbiased) estimator requires knowledge of the misclassification error rates, which are obtained by resampling from the initial sample and applying an infallible classifier. Tenenbein's "double sampling" methodology resamples randomly from the initial sample. We show that substantial gains in efficiency are obtainable if, instead of simple random resampling, different resampling rates are allowed for the fallibly determined classes in the initial sample. In most situations, this resampling methodology is neither more difficult nor more expensive than the double sampling methodology. A very promising area of application of the proposed methodology is sampling inspection in quality control, where products can either be classified quickly by inspectors or by a more thorough, perhaps destructive, inspection.
引用
收藏
页码:473 / 483
页数:11
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