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Exact solution of the donor-limited nearest neighbor hot deck imputation problem

Jan Pablo Burgard(burgardj***at***uni-trier.de)
Sven de Vries(devries***at***uni-trier.de)
Ulf Friedrich(ulf.friedrich***at***tum.de)
Dennis Kreber(kreberd***at***uni-trier.de)

Abstract: Data quality in population surveys suffers from missing responses. We use combinatorial optimization to create a complete and coherent data set. The methods are based on the widespread nearest neighbor hot deck imputation method that replaces the missing values with observed values from a close unit, the so-called donor. As a repeated use of donors may lead to distortions in the statistics, an additional constraint on the maximum number of donor re-uses is introduced. It is shown how this problem can be modeled as a maximum weighted b-matching problem. Based on this theoretical analysis, we implement a cost scaling, network simplex and successive shortest path algorithm for computing a globally optimal solution. These outperform the available and presently used implementations in terms of solution quality and running time. Statistical imputation is presented as a novel application of Operations Research, the available methodology is improved by a concise theoretical treatment, and fast implementations are provided.

Keywords: Survey Data, Combinatorial Optimization, b-Matching, Missing Data, Nearest Neighbor Hot Deck Imputation

Category 1: Combinatorial Optimization (Graphs and Matroids )

Category 2: Applications -- Science and Engineering (Statistics )

Category 3: Network Optimization

Citation:

Download: [PDF]

Entry Submitted: 08/20/2019
Entry Accepted: 08/20/2019
Entry Last Modified: 08/20/2019

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