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

Divergence-based confidence intervals in false-positive misclassification model Martín Apaolaza, Níriam Morales González, Domingo Pardo Llorente, Leandro 519.243 misclassification double sampling divergence estimators goodness-of-fit tests confidence intervals double sampling scheme binomial data goodness tests fit Estadística aplicada 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. Depto. de Estadística e Investigación Operativa Fac. de Ciencias Matemáticas TRUE pub 2023-06-20T09:43:53Z 2023-06-20T09:43:53Z 2008-05-21 journal article https://hdl.handle.net/20.500.14352/50263 0094-9655 10.1080/00949650601169622 metadata only access Gordon & Breach