percentielsplitsingen

Percentielsplitsingen is a statistical concept referring to the partitioning of a data set into segments defined by percentile thresholds. The term combines elements meaning percentile and splitting, and is used in some contexts to describe a nonparametric approach to dividing data for analysis.

Concept and method. The core idea is to select a set of percentile cut points (for example the 10th, 25th, 50th, 75th, and 90th percentiles) and assign each observation to a bin according to its percentile rank. The resulting groups may be designed to be approximately equal in size or adjusted to balance sample sizes across bins. In practice, cut points can be estimated from a training sample and then applied to a separate dataset. This approach emphasizes the distribution of values rather than their absolute magnitudes alone, and it can be used with or without smoothing to handle ties.

Applications and use cases. Percentielsplitsingen is used for exploratory data analysis, stratified or tiered analysis, and modeling scenarios where preserving distributional characteristics is important. It can support robust or nonparametric modeling, risk stratification, and visualization that highlights behavior across different parts of the distribution, such as tails or central regions.

Relation to related concepts and limitations. It is closely related to quantile binning, decile or quartile grouping, and quantile regression. Limitations include sensitivity to sample size and ties, potential instability of splits across samples, and the possibility of reducing information if too few or too many bins are used.

See also. See percentile, quantile, binning, decile, quartile. Further reading includes general texts on quantile-based data partitioning and nonparametric data analysis.