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Updated: May 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Optimality of group testing in the presence of misclassification
Aiyi Liu1, Chunling Liu, Zhiwei Zhang
1Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Rockville, Maryland 20852, U.S.A., liua@mail.nih.gov.
Abstract:
Several optimality properties of Dorfman's (1943) group testing procedure are derived for estimation of the prevalence of a rare disease whose status is classified with error. Exact ranges of disease prevalence are obtained for which group testing provides more efficient estimation when group size increases.
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