Abstract
Item nonresponses commonly trouble large-scale surveys and if corrections are performed extra variability is introduced in the estimates. When the imputed values are treated as if they were observed the precision of the estimates is generally overstated. Modification of a variance estimator for contemplating item nonresponses is a ticklish issue. There is not a common judgement on which is the best estimation method. Usually the imputation procedure, the variance estimator properties and the cost-effectiveness issues lead to choose a specific method. The paper shows a method based on grouped jackknife easy to implement, not computer intensive and suitable with random hot deck imputation. A simulative comparison on real business data with the bootstrap method with imputed data and the Multiple Imputation has been carried out. In the simulation the sampling strategy of Italian Small and Medium Enterprises survey has been taken into account and Taylor linearization technique when imputed data are treated as true values has been considered as well. The findings show that the proposed method has good performances with respect to the other ones and it outperforms them in terms of computational time spending. This paper summarizes some results of the BLUE-Enterprise and Trade Statistics project (BLUE-ETS project—Work Package WP6, http://www.blue-ets.istat.it).
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Righi, P., Falorsi, S., Fasulo, A. (2014). A Modified Extended Delete a Group Jackknife Variance Estimator Under Random Hot Deck Imputation in Business Surveys. In: Mecatti, F., Conti, P., Ranalli, M. (eds) Contributions to Sampling Statistics. Contributions to Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-05320-2_14
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