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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.
EmpiricalDistribution distribution constructed from all data
HistogramDistribution distribution constructed from binning of data
Nonparametric Kernel Density Estimates
SmoothKernelDistribution distribution with interpolated distribution functions
KernelMixtureDistribution distribution with mixture distribution functions
Distributions from Censored Data
SurvivalDistribution empirical distribution from censored data
Censoring create interval censored data from data and censoring information
Nonparametric Distribution Object
DataDistribution distribution object generated by nonparametric distributions
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