On the use of double sampling schemes in analyzing categorical data with misclassification errors

Y Hochberg - Journal of the American Statistical Association, 1977 - Taylor & Francis
Y Hochberg
Journal of the American Statistical Association, 1977Taylor & Francis
In order to resolve the difficulties involved in inference from a sample of categorical data
obtained by using a fallible classifying mechanism (usually inexpensive), we consider, as in
Tenenbein (1970, 1971, 1972), the utilization of an additional sample. The second sample is
subjected to a simultaneous cross-classification of its elements by the fallible mechanism
and by some true (usually expensive) classifying mechanism. The setup is general; ie, the
discussion can be applied to any multidimensional cross-classified data obtained by …
Abstract
In order to resolve the difficulties involved in inference from a sample of categorical data obtained by using a fallible classifying mechanism (usually inexpensive), we consider, as in Tenenbein (1970, 1971, 1972), the utilization of an additional sample. The second sample is subjected to a simultaneous cross-classification of its elements by the fallible mechanism and by some true (usually expensive) classifying mechanism. The setup is general; i.e., the discussion can be applied to any multidimensional cross-classified data obtained by unrestricted random sampling. Two methodologies are presented: (i) a combined maximum likelihood (ML) and least squares (LS) approach and (ii) a complete-LS approach. Both methodologies are illustrated using real data.
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