Analysis of multivariate categorical data with misclassification errors by triple sampling schemes

Autor:	Yosef Hochberg, T. Timothy Chen, Aaron Tenenbein
Rok vydání:	1984
Předmět:	Statistics and Probability Multivariate statistics Applied Mathematics Maximum likelihood Sampling (statistics) Inference Sample (statistics) computer.software_genre Statistics Log-linear model Data mining Statistics Probability and Uncertainty Categorical variable computer Mathematics
Zdroj:	Journal of Statistical Planning and Inference. 9:177-184
ISSN:	0378-3758
DOI:	10.1016/0378-3758(84)90018-1
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::4ffdf735d07e46415ef86cd4418b1274 https://doi.org/10.1016/0378-3758(84)90018-1 Zobrazit plný text záznamu