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Segmented regression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable is partitioned into intervals and a separate line segment is fit to each interval. Segmented regression analysis can also be performed on multivariate data by partitioning the various ...
When only one independent variable is present, the results may look like: X < BP ==> Y = A 1.X + B 1 + R Y; X > BP ==> Y = A 2.X + B 2 + R Y; where BP is the breakpoint, Y is the dependent variable, X the independent variable, A the regression coefficient, B the regression constant, and R Y the residual of Y.
However efficient computation and joint estimation of all model parameters (including the breakpoints) may be obtained by an iterative procedure [6] currently implemented in the package segmented [7] for the R language. A variant of decision tree learning called model trees learns piecewise linear functions. [8]
Note that regression kinks (or kinked regression) can also mean a type of segmented regression, which is a different type of analysis. Final considerations. The RD design takes the shape of a quasi-experimental research design with a clear structure that is devoid of randomized experimental features.
The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that sense it is not a separate statistical linear model. The various multiple linear regression models may be compactly written as [1]
Regression analysis – use of statistical techniques for learning about the relationship between one or more dependent variables (Y) and one or more independent variables (X). Overview articles [ edit ]
In statistics, multivariate adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. [1] It is a non-parametric regression technique and can be seen as an extension of linear models that automatically models nonlinearities and interactions between variables.
Segmented regression; Seismic inversion; Self-similarity matrix; Semantic mapping (statistics) Semantic relatedness; Semantic similarity; Semi-Markov process; Semi-log graph; Semidefinite embedding; Semimartingale; Semiparametric model; Semiparametric regression; Semivariance; Sensitivity (tests) Sensitivity analysis; Sensitivity and ...