P-index: A Generalized Statistical Aggregation Metric

Abstract

Data aggregation serves as a crucial technique in data analysis, particularly in the context of data dimensionality reduction. The most common approach involves employing statistical aggregation metrics such as mean, median, and others to characterize a dataset. However, different statistical aggregation metrics may yield disparate results, making the discrimination and selection of suitable metrics for describing data a research topic. Addressing this, the paper proposes a generalized statistical aggregation metric, categorizing several commonly used metrics under specific scenarios. This model offers a novel perspective for choosing among different statistical aggregation metrics and assists researchers in developing a deeper understanding of common statistical aggregation metrics. The introduction of this generalized metric provides a flexible and comprehensive method for considering multiple statistical measures simultaneously, enabling a more accurate grasp of the dataset’s essence and offering reliable insights for decision-making. The study contributes a new viewpoint to the selection and application of statistical aggregation metrics, advancing discussions in multi-objective optimization and data interpretability.