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- Q7135228 subject Q5645936.
- Q7135228 subject Q7483213.
- Q7135228 abstract "In statistics, a parametric model or parametric family or finite-dimensional model is a family of distributions that can be described using a finite number of parameters. These parameters are usually collected together to form a single k-dimensional parameter vector θ = (θ1, θ2, …, θk).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: in a “parametric” model all the parameters are in finite-dimensional parameter spaces; a model is “non-parametric” if all the parameters are in infinite-dimensional parameter spaces; a “semi-parametric” model contains finite-dimensional parameters of interest and infinite-dimensional nuisance parameters; a “semi-nonparametric” model has both finite-dimensional and infinite-dimensional unknown parameters of interest.Some statisticians believe that the concepts “parametric”, “non-parametric”, and “semi-parametric” are ambiguous. It can also be noted that the set of all probability measures has cardinality of continuum, and therefore it is possible to parametrize any model at all by a single number in (0,1) interval. This difficulty can be avoided by considering only “smooth” parametric models.".
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- Q7135228 wikiPageWikiLink Q7483213.
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- Q7135228 comment "In statistics, a parametric model or parametric family or finite-dimensional model is a family of distributions that can be described using a finite number of parameters. These parameters are usually collected together to form a single k-dimensional parameter vector θ = (θ1, θ2, …, θk).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.".
- Q7135228 label "Parametric model".