Editing Nonparametric statistics (section)

==Non-parametric models==
''Non-parametric models'' differ from [[parametric statistics|parametric]] models in that the model structure is not specified ''a priori'' but is instead determined from data. The term ''non-parametric'' is not meant to imply that such models completely lack parameters but that the number and nature of the parameters are flexible and not fixed in advance.
* A [[histogram]] is a simple nonparametric estimate of a probability distribution.
* [[Kernel density estimation]] is another method to estimate a probability distribution.
* [[Nonparametric regression]] and [[semiparametric regression]] methods have been developed based on [[kernel (statistics)|kernels]], [[Spline (mathematics)|splines]], and [[wavelet]]s.
* [[Data envelopment analysis]] provides efficiency coefficients similar to those obtained by [[multivariate analysis]] without any distributional assumption.
* [[k-nearest neighbors algorithm|KNNs]] classify the unseen instance based on the K points in the training set which are  nearest to it.
* A [[support vector machine]] (with a Gaussian kernel) is a nonparametric large-margin classifier.
* The [[method of moments (statistics)|method of moments]] with polynomial probability distributions.