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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.
As one of the leading experts in econometrics, her research focuses on econometric theory, Semi/nonparametric estimation and inference methods, Sieve methods, Nonlinear time series, and Semi/nonparametric models. [2] She was elected to the American Academy of Arts and Sciences in 2019. [3]
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 θ ...
They also applied the smoothing spline technique for their research. There was a case of application of partially linear model in biometrics by Zeger and Diggle in 1994. The research objective of their paper is the evolution period cycle of CD4 cell amounts in HIV (Human immune-deficiency virus) seroconverters (Zeger and Diggle, 1994). [3]
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;
Non-parametric statistics; Non-response bias; Non-sampling error; Nonparametric regression; Nonprobability sampling; Normal curve equivalent; Normal distribution; Normal probability plot – see also rankit; Normal score – see also rankit and Z score; Normal variance-mean mixture; Normal-exponential-gamma distribution; Normal-gamma distribution
In these approaches, the task is to estimate the parameters of the model that describes the stochastic process. When using the semi-parametric methods, the underlying process is modeled using a non-parametric framework, with the additional assumption that the number of non-zero components of the model is small (i.e., the model is sparse).
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 ...