nonlinear regression
Nonlinear regression
From Wikipedia, the free encyclopedia
Regression analysis
Linear regression.svg
Models
Linear regression Simple regression Ordinary least squares Polynomial regression General linear model
Generalized linear model Discrete choice Logistic regression Multinomial logit Mixed logit Probit Multinomial probit Ordered logit Ordered probit Poisson
Multilevel model Fixed effects Random effects Mixed model
Nonlinear regression Nonparametric Semiparametric Robust Quantile Isotonic Principal components Least angle Local Segmented
Errors-in-variables
Estimation
Least squares Ordinary least squares Linear (math) Partial Total Generalized Weighted Non-linear Iteratively reweighted Ridge regression LASSO
Least absolute deviations Bayesian Bayesian multivariate
Background
Regression model validation Mean and predicted response Errors and residuals Goodness of fit Studentized residual Gauss–Markov theorem
Portal icon Statistics portal v t e
See Michaelis-Menten kinetics for details
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations.
Contents [hide]
1 General
2 Regression statistics
3 Ordinary and weighted least squares
4 Linearization
4.1 Transformation
4.2 Segmentation
5 See also
6 References
7 Further reading
General[edit]
The data consist of error-free independent variables (explanatory variables), x, and their associated observed dependent variables (response variables), y. Each y is modeled as a random variable with a mean given by a nonlinear function f(x,β). Systematic error may be present but its treatment is outside the scope of regression analysis. If the independent variables are not error-free, this is an errors-in-variables model, also outside this scope.
For example,
From Wikipedia, the free encyclopedia
Regression analysis
Linear regression.svg
Models
Linear regression Simple regression Ordinary least squares Polynomial regression General linear model
Generalized linear model Discrete choice Logistic regression Multinomial logit Mixed logit Probit Multinomial probit Ordered logit Ordered probit Poisson
Multilevel model Fixed effects Random effects Mixed model
Nonlinear regression Nonparametric Semiparametric Robust Quantile Isotonic Principal components Least angle Local Segmented
Errors-in-variables
Estimation
Least squares Ordinary least squares Linear (math) Partial Total Generalized Weighted Non-linear Iteratively reweighted Ridge regression LASSO
Least absolute deviations Bayesian Bayesian multivariate
Background
Regression model validation Mean and predicted response Errors and residuals Goodness of fit Studentized residual Gauss–Markov theorem
Portal icon Statistics portal v t e
See Michaelis-Menten kinetics for details
In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations.
Contents [hide]
1 General
2 Regression statistics
3 Ordinary and weighted least squares
4 Linearization
4.1 Transformation
4.2 Segmentation
5 See also
6 References
7 Further reading
General[edit]
The data consist of error-free independent variables (explanatory variables), x, and their associated observed dependent variables (response variables), y. Each y is modeled as a random variable with a mean given by a nonlinear function f(x,β). Systematic error may be present but its treatment is outside the scope of regression analysis. If the independent variables are not error-free, this is an errors-in-variables model, also outside this scope.
For example,