Résultat de la recherche
Calibrated random imputation for qualitative data
In official statistics, when a file of microdata must be delivered to external users, it is very difficult to propose them a file where missing values has been treated by multiple imputations. In order to overcome this difficulty, we propose a method of single imputation for qualitative data that respect numerous constraints. The imputation is balanced on totals previously estimated; editing rules can be respected; the imputation is random, but the totals are not affected by an imputation variance.
Coordination, combination and extension of optimal balanced samples
Optimal allocation in balanced sampling
The development of new sampling methods allows the selection of large balanced samples. In this paper we propose a method for computing optimal inclusion probabilities for balanced samples. Next, we show that the optimal Neyman allocation is a particular case of this method.
Les principales techniques de traitement de la non-réponse
A variant of the Cox algorithm for the imputation of non-response of qualitative data
The Coxalgorithm allows to round randomly and unbiasedly a table of real numbers without modifying the marginal totals. One possible use of this method is the random imputation of aqualitative variable in survey sampling. A modification of the Coxalgorithm is proposed in order to take into account a weighting system, which is commonly used in survey sampling. The use of this new method allows to construct a controlled imputation method that reduces the imputation variance.