Prediction of Finite Population Proportion When Responses Are Misclassified
Keyword(s):
Abstract We propose a model-based predictive estimator of the finite population proportion of a misclassified binary response, when information on the auxiliary variable(s) is available for all units in the population. Asymptotic properties of the misclassification-adjusted predictive estimator are also explored. We propose a computationally efficient bootstrap variance estimator that exhibits better performance compared to usual analytical variance estimator. The performance of the proposed estimator is compared with other commonly used design-based estimators through extensive simulation studies. The results are supplemented by an empirical study based on literacy data.
2018 ◽
2019 ◽
Vol 18
(2)
◽
2017 ◽
Vol 91
(3)
◽
pp. 354-365
◽
2018 ◽
Vol 7
(4)
◽
pp. 104
2007 ◽
Vol 98
(7)
◽
pp. 1417-1440
◽
2016 ◽
Vol 40
(1)
◽
pp. 318-330
◽