Data matching is a typical statistical problem in non experimental and/or observational studies or, more generally, in cross-sectional studies in which one or more data sets are to be compared. Several methods are available in the literature, most of which based on a particular metric or on statistical models, either parametric or nonparametric. We present two methods to calculate a proximity which have the property of being invariant under monotonic transformations. These methods require at most the notion of ordering. We provide an open-source software in the form of a R package. The software is available at: https://r-forge.r-project.org/projects/rrp/ See also: PORRO G., IACUS S.M (2008). Invariant and metric free proximities for data matching: an R package. JOURNAL OF STATISTICAL SOFTWARE., vol. 25 (11), p. 1-22, ISSN: 1548-7660
Random Recursive Partitiong and Rank-based proximities for data matching, missing data imputation and nonparametric classification and prediction
PORRO, GIUSEPPE
2008-01-01
Abstract
Data matching is a typical statistical problem in non experimental and/or observational studies or, more generally, in cross-sectional studies in which one or more data sets are to be compared. Several methods are available in the literature, most of which based on a particular metric or on statistical models, either parametric or nonparametric. We present two methods to calculate a proximity which have the property of being invariant under monotonic transformations. These methods require at most the notion of ordering. We provide an open-source software in the form of a R package. The software is available at: https://r-forge.r-project.org/projects/rrp/ See also: PORRO G., IACUS S.M (2008). Invariant and metric free proximities for data matching: an R package. JOURNAL OF STATISTICAL SOFTWARE., vol. 25 (11), p. 1-22, ISSN: 1548-7660
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