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  2. Semiparametric model - Wikipedia

    en.wikipedia.org/wiki/Semiparametric_model

    In statistics, a semiparametric model is a statistical model that has parametric and nonparametric components. A statistical model is a parameterized family of distributions: { P θ : θ ∈ Θ } {\displaystyle \{P_{\theta }:\theta \in \Theta \}} indexed by a parameter θ {\displaystyle \theta } .

  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. Predictive modelling - Wikipedia

    en.wikipedia.org/wiki/Predictive_modelling

    Broadly speaking, there are two classes of predictive models: parametric and non-parametric. 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]

  5. 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.

  6. Partially linear model - Wikipedia

    en.wikipedia.org/wiki/Partially_linear_model

    The real-world application of partially linear model was first considered for analyzing data by Engle, Granger, Rice and Weiss in 1986. [2]In their point of view, the relevance between temperature and the consumption of electricity cannot be expressed in a linear model, because there are massive of confounding factors, such as average income, goods price, consumer purchase ability and some ...

  7. 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;

  8. Generalized additive model - Wikipedia

    en.wikipedia.org/wiki/Generalized_additive_model

    The functions f i may be functions with a specified parametric form (for example a polynomial, or an un-penalized regression spline of a variable) or may be specified non-parametrically, or semi-parametrically, simply as 'smooth functions', to be estimated by non-parametric means.

  9. Category:Nonparametric statistics - Wikipedia

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

    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.