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  2. Nonparametric regression - Wikipedia

    en.wikipedia.org/wiki/Nonparametric_regression

    That is, no parametric equation is assumed for the relationship between predictors and dependent variable. Nonparametric regression requires larger sample sizes than regression based on parametric models because the data must supply the model structure as well as the parameter estimates.

  3. Nonparametric statistics - Wikipedia

    en.wikipedia.org/wiki/Nonparametric_statistics

    non-parametric regression, which is modeling whereby the structure of the relationship between variables is treated non-parametrically, but where nevertheless there may be parametric assumptions about the distribution of model residuals. non-parametric hierarchical Bayesian models, such as models based on the Dirichlet process, which allow the ...

  4. Parametric model - Wikipedia

    en.wikipedia.org/wiki/Parametric_model

    Parametric models are contrasted with the semi-parametric, semi-nonparametric, and non-parametric models, all of which consist of an infinite set of "parameters" for description. The distinction between these four classes is as follows: [citation needed] in a "parametric" model all the parameters are in finite-dimensional parameter spaces;

  5. Parametric statistics - Wikipedia

    en.wikipedia.org/wiki/Parametric_statistics

    Parametric statistical methods are used to compute the 2.33 value above, given 99 independent observations from the same normal distribution. A non-parametric estimate of the same thing is the maximum of the first 99 scores. We don't need to assume anything about the distribution of test scores to reason that before we gave the test it was ...

  6. Semiparametric regression - Wikipedia

    en.wikipedia.org/wiki/Semiparametric_regression

    In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations where the fully nonparametric model may not perform well or when the researcher wants to use a parametric model but the functional form with respect to a subset of the regressors or the density of the errors is not known.

  7. Decision tree learning - Wikipedia

    en.wikipedia.org/wiki/Decision_tree_learning

    Possible to validate a model using statistical tests. That makes it possible to account for the reliability of the model. Non-parametric approach that makes no assumptions of the training data or prediction residuals; e.g., no distributional, independence, or constant variance assumptions; Performs well with large datasets.

  8. Predictive modelling - Wikipedia

    en.wikipedia.org/wiki/Predictive_modelling

    A third class, semi-parametric models, includes features of both. Parametric models make "specific assumptions with regard to one or more of the population parameters that characterize the underlying distribution(s)". [3] Non-parametric models "typically involve fewer assumptions of structure and distributional form [than parametric models] but ...

  9. Category:Nonparametric statistics - Wikipedia

    en.wikipedia.org/wiki/Category:Nonparametric...

    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 ...