Parametric model

In statistics, a parametric model or parametric family or finite-dimensional model is a particular class of statistical models. Specifically, a parametric model is a family of probability distributions that has a finite number of parameters. A statistical model is a collection of probability distributions on some sample space. We assume that the collection, P, is indexed by some set Θ. The set Θ is called the parameter set or, more commonly, the parameter space. For each θ ∈ Θ, let Fθ denote the corresponding member of the collection; so Fθ is a cumulative distribution function. Then a statistical model can be written as The model is a parametric model if Θ ⊆ Rk for some positive integer k. When the model consists of absolutely continuous distributions, it is often specified in terms of corresponding probability density functions: The Poisson family of distributions is parametrized by a single number λ > 0: where pλ is the probability mass function. This family is an exponential family. The normal family is parametrized by θ = (μ, σ), where μ ∈ R is a location parameter and σ > 0 is a scale parameter: This parametrized family is both an exponential family and a location-scale family. The Weibull translation model has a three-dimensional parameter θ = (λ, β, μ): The binomial model is parametrized by θ = (n, p), where n is a non-negative integer and p is a probability (i.e. p ≥ 0 and p ≤ 1): This example illustrates the definition for a model with some discrete parameters. A parametric model is called identifiable if the mapping θ ↦ Pθ is invertible, i.e. there are no two different parameter values θ1 and θ2 such that Pθ1 = Pθ2. 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.