2026-06-07T21:21:35Zhttps://docta.ucm.es/rest/oai/request

oai:docta.ucm.es:20.500.14352/502632023-08-25T13:43:49Zcom_20.500.14352_14col_20.500.14352_15

Martín Apaolaza, Níriam Morales González, Domingo 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. metadata only access Divergence-based confidence intervals in false-positive misclassification model journal article