Linked from

The 12 pages that link to Empirical distribution function, each with the reason it gives.

• Probability distributionCompared with: It is computed from observed data rather than specified as a model for random outcomes.

• Sampling distributionCompared with: It describes observed data values, not the variation of a statistic across hypothetical samples.

• Cumulative distribution functionBroader topic: It estimates an unknown population CDF directly from observed data.

• MedianRelated: Its halfway crossing identifies sample medians without requiring a continuous distribution.

• Convergence in probabilityRelated: For each fixed point, it converges in probability to the population distribution function under standard sampling assumptions.

• Gini coefficientRelated: Sample-based Gini estimates depend on how observed values represent the underlying distribution.

• Order statisticRelated: Its jumps record the ranks and repeated values that determine order statistics.

• Nonparametric statisticsBroader topic: It estimates a distribution directly from observations without choosing a named distribution family.

• QuartileRelated: Quartile estimates can be defined as inverse points of this sample distribution.

• Law of the iterated logarithmRelated: Iterated-logarithm results describe almost-sure fluctuations of empirical distributions around their population values.

• Glivenko–Cantelli theoremBroader topic: It is the random function whose uniform distance from the population distribution vanishes.

• Family (statistics)Compared with: It summarizes observed data without requiring a chosen parametric family.