2026-06-08T03:46:31Zhttps://docta.ucm.es/rest/oai/requestoai:docta.ucm.es:20.500.14352/502632023-08-25T13:43:49Zcom_20.500.14352_14col_20.500.14352_15
Docta Complutense
author
Martín Apaolaza, Níriam
author
Morales González, Domingo
author
Pardo Llorente, Leandro
2023-06-20T09:43:53Z
2023-06-20T09:43:53Z
2008-05-21
0094-9655
10.1080/00949650601169622
https://hdl.handle.net/20.500.14352/50263
http://www.tandfonline.com/doi/full/10.1080/00949650601169622#
http://www.tandfonline.com/
In this article, we introduce minimum divergence estimators of parameters of a binary response model when data are subject to false-positive misclassification and obtained using a double-sampling plan. Under this set up, the problem of goodness-of-fit is considered and divergence-based confidence intervals (CIs) for a population proportion parameter are derived. A simulation experiment is carried out to compare the coverage probabilities of the new CIs. An application to real data is also given.
Divergence-based confidence intervals in false-positive misclassification model
journal article