Martín Apaolaza, NíriamMorales González, DomingoPardo Llorente, Leandro2023-06-202023-06-202008-05-210094-965510.1080/00949650601169622https://hdl.handle.net/20.500.14352/50263In 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.journal articlehttp://www.tandfonline.com/doi/full/10.1080/00949650601169622#http://www.tandfonline.com/metadata only access519.243misclassificationdouble samplingdivergence estimatorsgoodness-of-fit testsconfidence intervalsdouble sampling schemebinomial datagoodnesstestsfitEstadística aplicada