On the Use of Double Sampling Schemes in Analyzing Categorical Data with Misclassification Errors
作者:
Yosef Hochberg,
期刊:
Journal of the American Statistical Association
(Taylor Available online 1977)
卷期:
Volume 72,
issue 360
页码: 914-921
ISSN:0162-1459
年代: 1977
DOI:10.1080/01621459.1977.10479983
出版商: Taylor & Francis Group
关键词: Fixed-bias errors;Two-stage inference;Maximum likelihood;Least squares
数据来源: Taylor
摘要:
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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