Functional regression is a version of regression analysis when responses or covariates include functional data. 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 linear, partially linear, or nonlinear. In particular, functional polynomial models, functional single and multiple index models and functional additive models are three special cases of functional nonlinear models.
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Functionalregression is a version of regression analysis when responses or covariates include functional data. Functionalregression models can be classified...
to extending linear regression model to polynomial regression model. For a scalar response Y {\displaystyle Y} and a functional covariate X ( ⋅ ) {\displaystyle...
Regression testing (rarely, non-regression testing) is re-running functional and non-functional tests to ensure that previously developed and tested software...
expansion. FPCA can be applied for representing random functions, or in functionalregression and classification. For a square-integrable stochastic process X(t)...
(e.g., nonparametric regression). Regression analysis is primarily used for two conceptually distinct purposes. First, regression analysis is widely used...
linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where...
not restricted to functional testing. Functional testing includes but is not limited to: Sanity testing, a.k.a. smoke testing Regression testing Usability...
Look up regression, regressions, or régression in Wiktionary, the free dictionary. Regression or regressions may refer to: Marine regression, coastal advance...
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...
regression analysis, are acceptable as descriptions of the data. The validation process can involve analyzing the goodness of fit of the regression,...
In statistics, regression toward the mean (also called regression to the mean, reversion to the mean, and reversion to mediocrity) is the phenomenon where...
Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...
model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is...
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...
In statistics and numerical analysis, isotonic regression or monotonic regression is the technique of fitting a free-form line to a sequence of observations...
combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic...
In statistics, semiparametric regression includes regression models that combine parametric and nonparametric models. They are often used in situations...
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination...
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...
algorithms as "functional gradient boosting". Friedman et al. describe an advancement of gradient boosted models as Multiple Additive Regression Trees (MART);...
In robust statistics, robust regression seeks to overcome some limitations of traditional regression analysis. A regression analysis models the relationship...
especially in the case of a simple linear regression, in which there is a single regressor on the right side of the regression equation. The OLS estimator is consistent...
Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to...
In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...
Partial least squares (PLS) regression is a statistical method that bears some relation to principal components regression; instead of finding hyperplanes...
distinction is most important in regression analysis, where the concepts are sometimes called the regression errors and regression residuals and where they lead...
parametric (normally polynomial regression). The most common non-parametric method used in the RDD context is a local linear regression. This is of the form: Y...
Functional Linear Regression, Functional Poisson Regression and Functional Binomial Regression, with the important Functional Logistic Regression included...
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables...
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