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 independent variables. Segmented regression is useful when the independent variables, clustered into different groups, exhibit different relationships between the variables in these regions. The boundaries between the segments are breakpoints.
Segmented linear regression is segmented regression whereby the relations in the intervals are obtained by linear regression.
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Segmentedregression, also known as piecewise regression or broken-stick regression, is a method in regression analysis in which the independent variable...
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination...
Local regression or local polynomial regression, also known as moving regression, is a generalization of the moving average and polynomial regression. Its...
linear regression; for more than one, the process is called multiple linear regression. This term is distinct from multivariate linear regression, where...
application software SegReg is a free and user-friendly tool for linear segmentedregression analysis to determine the breakpoint where the relation between the...
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample...
Ridge regression is a method of estimating the coefficients of multiple-regression models in scenarios where the independent variables are highly correlated...
aggregate behavior (for example, public opinion). The models used in segmentedregression analysis are threshold models. Certain deterministic recursive multivariate...
Deming regression Scale invariance: Major axis regression Linear least squares Linear segmentedregression Linear trend estimation Polynomial regression Regression...
In statistics, ordinal regression, also called ordinal classification, is a type of regression analysis used for predicting an ordinal variable, i.e....
Quantile regression is a type of regression analysis used in statistics and econometrics. Whereas the method of least squares estimates the conditional...
adaptive regression splines (MARS) is a form of regression analysis introduced by Jerome H. Friedman in 1991. It is a non-parametric regression technique...
combination of one or more independent variables. In regression analysis, logistic regression (or logit regression) is estimating the parameters of a logistic...
data with random variation the tolerance level can be found with segmentedregression. As the Maas-Hoffman model is fitted to the data by the method of...
Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. Poisson regression assumes...
Bockerman et al. (2018). Note that regression kinks (or kinked regression) can also mean a type of segmentedregression, which is a different type of analysis...
"multilevel regression" and "poststratification" ideas of MRP can be generalized. Multilevel regression can be replaced by nonparametric regression or regularized...
In statistics, multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than...
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...
In statistics, polynomial regression is a form of regression analysis in which the relationship between the independent variable x and the dependent variable...
In statistics, binomial regression is a regression analysis technique in which the response (often referred to as Y) has a binomial distribution: it is...
In statistics, specifically regression analysis, a binary regression estimates a relationship between one or more explanatory variables and a single output...
(WLS), also known as weighted linear regression, is a generalization of ordinary least squares and linear regression in which knowledge of the unequal variance...
^{\mathsf {T}}\mathbf {y} .} Optimal instruments regression is an extension of classical IV regression to the situation where E[εi | zi] = 0. Total least...
Partial least squares regression (PLS regression) is a statistical method that bears some relation to principal components regression; instead of finding...
model (also ordered logistic regression or proportional odds model) is an ordinal regression model—that is, a regression model for ordinal dependent variables—first...
piecewise linear or segmented function is a real-valued function of a real variable, whose graph is composed of straight-line segments. A piecewise linear...
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