Féligparaméteres

Féligparaméteres is a Hungarian term that translates to "semiparametric" in English. It describes a class of statistical models that combine aspects of both parametric and non-parametric approaches. In a parametric model, the functional form of the relationship between variables is fully specified in advance, typically involving a finite number of parameters to be estimated. For example, a linear regression model is parametric because it assumes a linear relationship y = β₀ + β₁x + ε.

In contrast, non-parametric models make fewer assumptions about the functional form, allowing the data to dictate the shape of the relationship. Examples include kernel regression or decision trees. A semiparametric model, therefore, is one where some components are specified parametrically, while others are left unspecified and are estimated non-paramarametrically. This offers a balance, leveraging the efficiency and interpretability of parametric components while retaining the flexibility of non-parametric methods for parts of the model where strong assumptions might be inappropriate or lead to misspecification.

A common example of a semiparametric model is the partially linear model, which can be expressed as y = Xβ + g(Z) + ε, where Xβ represents the parametric part and g(Z) represents a non-parametric function of Z. This type of model is useful when we have some variables whose relationship with the response is well understood and can be modeled parametrically, while other variables might have a more complex or unknown relationship that is better handled non-parametrically. Semiparametric models are widely used in various fields, including econometrics, biostatistics, and machine learning, due to their ability to provide robust and flexible modeling solutions.