Nonparametric Statistical Distributions

When building an initial statistical model, you may not have a good idea of what parametric distribution family it should come from. Nonparametric distributions make very few assumptions about the underlying model so can be used for a wide variety of situations. Nonparametric distributions are based on familiar methods such as histograms and kernel density estimators. The resulting distributions work just like any other distribution in that you can generate random variates, compute moments and quantiles, or compute full distribution functions. Different nonparametric distributions essentially provide different levels and methods of smoothing.

Empirical Distribution

(RefLink[EmpiricalDistribution, paclet:ref/EmpiricalDistribution]) — distribution constructed from all data

Histogram Distribution

(RefLink[HistogramDistribution, paclet:ref/HistogramDistribution]) — distribution constructed from binning of data

Smooth Kernel Distributions

(RefLink[SmoothKernelDistribution, paclet:ref/SmoothKernelDistribution]) — distribution with interpolated distribution functions

(RefLink[KernelMixtureDistribution, paclet:ref/KernelMixtureDistribution]) — distribution with mixture distribution functions

Distributions from Censored Data

(RefLink[SurvivalDistribution, paclet:ref/SurvivalDistribution]) — empirical distribution from censored data

(RefLink[SurvivalModelFit, paclet:ref/SurvivalModelFit]) — survival distribution with confidence intervals

(RefLink[CoxModelFit, paclet:ref/CoxModelFit]) — survival distribution with independent variables

Nonparametric Distribution Object

(RefLink[DataDistribution, paclet:ref/DataDistribution]) — distribution object generated by nonparametric distributions

(OrangeLink[Machine-Learning Distributions, paclet:guide/MachineLearning])

(RefLink[LearnedDistribution, paclet:ref/LearnedDistribution]) — distribution for any kind of data (numerical, images, ...)