Non-parametric statistics
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The branch of statistics known as non-parametric statistics is concerned with non-parametric statistical models and non-parametric statistical tests.
Nonparametric models differ from parametric models in that the model structure is not specified a priori, but is instead determined from data. The term nonparametric is not meant to imply that such models completely lack parameters; rather, the number and nature of the parameters is flexible and not fixed in advance. Nonparametric models are therefore also called distribution free.
- A histogram is a simple nonparametric estimate of a probability distribution
- Stochastic kernels are commonly used in density estimation
- Nonparametric regression and semiparametric regression methods have been developed based on kernels, splines, and wavelets.
- binomial test
- Anderson-Darling test
- chi-square test
- Cochran's Q
- Cohen's kappa
- Fisher's exact test
- Friedman two-way analysis of variance by ranks
- Kendall's tau
- Kendall's W
- Kolmogorov-Smirnov test
- Kruskal-Wallis one-way analysis of variance by ranks
- Kuiper's test
- Mann-Whitney U or Wilcoxon rank sum test
- McNemar's test (a special case of the chi-squared test)
- median test
- Pitman's permutation test
- Siegel-Tukey test
- Spearman's rank correlation coefficient
- Wald-Wolfowitz runs test
- Wilcoxon signed-rank test
See also
- parametric statistics
- resampling (statistics)
- robust statistics
- particle filter for the general theory of Sequential Monte Carlo methods
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