|Nonparametric statistics is included in the JEL classification codes as JEL: C11|
Nonparametric statistics is a branch of statistics concerned with non-parametric statistical models and non-parametric statistical tests. Non-parametric statistics are statistics that do not estimate population parameters. In contrast, see parametric statistics.
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 but that the number and nature of the parameters are flexible and not fixed in advance. Nonparametric models are therefore also called distribution free.
Nonparametric (or distribution-free) inferential statistical methods are mathematical procedures for statistical hypothesis testing which, unlike parametric statistics, make no assumptions about the frequency distributions of the variables being assessed.
This category has the following 4 subcategories, out of 4 total.
Pages in category "Nonparametric statistics"
The following 68 pages are in this category, out of 68 total.
- Nonparametric statistics (computing)
- Geometric median (computing)
- K-nearest neighbors algorithm (computing)
- Kendall rank correlation coefficient (computing)
- Kendall's W (computing)
- Kernel (statistics) (computing)
- Kernel density estimation (computing)
- Kernel smoother (computing)
- Kolmogorov–Smirnov test (computing)
- Kruskal–Wallis one-way analysis of variance (computing)
- Kuiper's test (computing)
- U-statistic (computing)