Non-parametric models differ from 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 provides better estimates of the density than histograms.
- Nonparametric regression and semiparametric regression methods have been developed based on kernels, splines, and wavelets.
- Data envelopment analysis provides efficiency coefficients similar to those obtained by multivariate analysis without any distributional assumption.
Read more about this topic: Non-parametric Statistics
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