Analysis of multivariate categorical data with misclassification errors by triple sampling schemes |
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Authors: | T Timothy Chen Yosef Hochberg Aaron Tenenbein |
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Institution: | University of Texas System Cancer Center, Houston, TX, USA;Tel Aviv University, Tel Aviv, Israel;New York University, New York, NY, USA |
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Abstract: | Previous work has been carried out on the use of double-sampling schemes for inference from categorical data subject to misclassification. The double-sampling schemes utilize a sample of n units classified by both a fallible and true device and another sample of n2 units classified only by a fallible device. In actual applications, one often hasavailable a third sample of n1 units, which is classified only by the true device. In this article we develop techniques of fitting log-linear models under various misclassification structures for a general triple-sampling scheme. The estimation is by maximum likelihood and the fitted models are hierarchical. The methodology is illustrated by applying it to data in traffic safety research from a study on the effectiveness of belts in reducing injuries. |
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Keywords: | Primary 62D05 Secondary 62F05 62F10 Misclassification Triple-sampling Log-linear models |
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