RT Journal Article
T1 Divergence-based confidence intervals in false-positive misclassification model
A1 Martín Apaolaza, Níriam
A1 Morales González, Domingo
A1 Pardo Llorente, Leandro
AB 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.
PB Gordon & Breach
SN 0094-9655
YR 2008
FD 2008-05-21
LK https://hdl.handle.net/20.500.14352/50263
UL https://hdl.handle.net/20.500.14352/50263
DS Docta Complutense
RD 29 jun 2025