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Functional regression
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<p>Functional regression is a version of <a href="/facts/Regression_analysis/n6z5Tf7K">regression analysis</a> when <a href="/facts/Dependent_and_independent_variables/dINfzIF2">responses</a> or <a href="/facts/Dependent_and_independent_variables/dINfzIF2">covariates</a> include <a href="/facts/Functional_data_analysis/dPLDWYHn">functional data</a>. Functional regression models can be classified into four types depending on whether the responses or covariates are functional or scalar: (i) scalar responses with functional covariates, (ii) functional responses with scalar covariates, (iii) functional responses with functional covariates, and (iv) scalar or functional responses with functional and scalar covariates. In addition, functional regression models can be <a href="/facts/Linear_regression/5n998IhK">linear</a>, partially linear, or <a href="/facts/Nonlinear_regression/8mMuJSe8">nonlinear</a>. In particular, functional polynomial models, functional <a href="/facts/Semiparametric_regression/jku1M9mb">single and multiple index models</a> and functional <a href="/facts/Additive_model/fUwPDaSu">additive models</a> are three special cases of functional nonlinear models. </p>