A common problem faced by statistical offices is that data may be missing from collected data
sets. The typical way to overcome this problem is to impute the missing data. The problem
of imputing missing data is complicated by the fact that statistical data often have to satisfy
certain edit rules, which for numerical data usually take the form of linear restrictions. Standard
imputation methods generally do not take such edit restrictions into account. In the present article
we describe two general approaches for imputation of missing numerical data that do take the edit
restrictions into account. The first approach imputes the missing values by means of an imputation
method and afterwards adjusts the imputed values so they satisfy the edit restrictions. The second
approach sequentially imputes the missing data. It uses Fourier-Motzkin elimination to determine
appropriate intervals for each variable to be imputed. Both approaches are not based on a specific
imputation model, but allow one to specify an imputation model. To illustrate the two approaches
we assume that the data are approximately multivariately normally distributed. To assess the
performance of the imputation approaches an evaluation study is carried out.
CitationCoutinho, Wieger; De Waal, Ton; Remmerswaal, Marco. Imputation of numerical data under linear edit restrictions. "SORT", vol. 35, núm. 1, p. 39-62.
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