Abstract
Typically a prioritization between collections of objects involves several parameters and requires thus the application of multicriteria methodologies. Partial order ranking offers a non-parametric method that neither includes any assumptions about linearity nor any assumptions about distribution properties. Accumulating partial order ranking (APOR) is a novel technique where data from a series of individual tests of various characteristics are aggregated, however, maintaining the basics of the partial order ranking methodology. APOR offers prioritization based on mutual probabilities derived from the aggregated data. Alternatively prioritization may be achieved based on averaged ranks derived from the APOR. The present study illustrates the application APOR by an assessment of a series of chemical substances.
Published Version
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