Statistical analysis with missing data
Statistical analysis with missing data
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
A Branch and Bound Algorithm for Feature Subset Selection
IEEE Transactions on Computers
Nearest neighbor pattern classification
IEEE Transactions on Information Theory
Editorial: Special Issue on Statistical Algorithms and Software
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Editorial: Second special issue on statistical algorithms and software
Computational Statistics & Data Analysis
Uncertainty analysis for statistical matching of ordered categorical variables
Computational Statistics & Data Analysis
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Data fusion concerns the problem of merging information coming from independent sources. Also known as statistical matching, file grafting or microdata merging, it is a challenging problem for statisticians. The increasing growth of collected data makes combining different sources of information an attractive alternative to single source data. The interest in data fusion derives, in certain cases, from the impossibility of attaining specific information from one source of data and the reduction of the cost entailed by this operation and, in all cases, from taking greater advantage of the available collected information. The GRAFT system is presented. It is a multipurpose data fusion system based on the k-nearest neighbor (k-nn) hot deck imputation method. The system aim is to cope with many data fusion problems and domains. The k-nn is a very demanding algorithm. The solutions envisaged and their cost, which allow this methodology to be used in a wide range of real problems, are presented.