Semiparametric model

In statistics, a semiparametric model is a statistical model that has parametric and nonparametric components.

A statistical model is a collection of distributions: $\{P_{\theta }:\theta \in \Theta \}$ indexed by a parameter $\theta$ .

A parametric model is one in which the indexing parameter is a finite-dimensional vector (in $k$ -dimensional Euclidean space for some integer $k$ ); i.e. the set of possible values for $\theta$ is a subset of $\mathbb {R} ^{k}$ , or $\Theta \subset {\mathbb {R}}^{k}$ . In this case we say that $\theta$ is finite-dimensional.
In nonparametric models, the set of possible values of the parameter $\theta$ is a subset of some space, not necessarily finite-dimensional. For example, we might consider the set of all distributions with mean 0. Such spaces are vector spaces with topological structure, but may not be finite-dimensional as vector spaces. Thus, $\Theta \subset {\mathbb {F}}$ for some possibly infinite-dimensional space $\mathbb {F}$ .
In semiparametric models, the parameter has both a finite-dimensional component and an infinite-dimensional component (often a real-valued function defined on the real line). Thus the parameter space $\Theta$ in a semiparametric model satisfies $\Theta \subset {\mathbb {R}}^{k}\times {\mathbb {F}}$ , where $\mathbb {F}$ is an infinite-dimensional space.

It may appear at first that semiparametric models include nonparametric models, since they have an infinite-dimensional as well as a finite-dimensional component. However, a semiparametric model is considered to be "smaller" than a completely nonparametric model because we are often interested only in the finite-dimensional component of $\theta$ . That is, we are not interested in estimating the infinite-dimensional component. In nonparametric models, by contrast, the primary interest is in estimating the infinite-dimensional parameter. Thus the estimation task is statistically harder in nonparametric models.

These models often use smoothing or kernels.

Example

A well-known example of a semiparametric model is the Cox proportional hazards model.^[1] If we are interested in studying the time $T$ to an event such as death due to cancer or failure of a light bulb, the Cox model specifies the following distribution function for $T$ :

F(t)=1-\exp \left(-\int _{0}^{t}\lambda _{0}(u)e^{{\beta 'x}}du\right),

where $x$ is the covariate vector, and $\beta$ and $\lambda _{0}(u)$ are unknown parameters. $\theta =(\beta ,\lambda _{0}(u))$ . Here $\beta$ is finite-dimensional and is of interest; $\lambda _{0}(u)$ is an unknown non-negative function of time (known as the baseline hazard function) and is often a nuisance parameter. The collection of possible candidates for $\lambda _{0}(u)$ is infinite-dimensional.

References

↑ N. Balakrishnan; C.R. Rao (30 January 2004). Handbook of Statistics: Advances in Survival Analysis. Elsevier. p. 126. ISBN 978-0-08-049511-8.

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[BalakrishnanRao2004-1] N. Balakrishnan; C.R. Rao (30 January 2004). Handbook of Statistics: Advances in Survival Analysis. Elsevier. p. 126. ISBN 978-0-08-049511-8.

Semiparametric model

Example

See also

References