# Nonlinear regression

(Redirected from Non-linear regression)
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.

## General

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, the Michaelis–Menten model for enzyme kinetics

$v = \frac{V_\max\ [\mbox{S}]}{K_m + [\mbox{S}]}$

can be written as

$f(x,\boldsymbol\beta)= \frac{\beta_1 x}{\beta_2 + x}$

where $\beta_1$ is the parameter $V_\max$, $\beta_2$ is the parameter $K_m$ and [S] is the independent variable, x. This function is nonlinear because it cannot be expressed as a linear combination of the $\beta$s.

Other examples of nonlinear functions include exponential functions, logarithmic functions, trigonometric functions, power functions, Gaussian function, and Lorenz curves. Some functions, such as the exponential or logarithmic functions, can be transformed so that they are linear. When so transformed, standard linear regression can be performed but must be applied with caution. See Linearization, below, for more details.

In general, there is no closed-form expression for the best-fitting parameters, as there is in linear regression. Usually numerical optimization algorithms are applied to determine the best-fitting parameters. Again in contrast to linear regression, there may be many local minima of the function to be optimized and even the global minimum may produce a biased estimate. In practice, estimated values of the parameters are used, in conjunction with the optimization algorithm, to attempt to find the global minimum of a sum of squares.

For details concerning nonlinear data modeling see least squares and non-linear least squares.

## Gaussian Kernel Regression (Gaussian Kernel Smoothing)

One popular and easy way is to use a kernel function. This kernel function transforms the nonlinear problem to the linear regression (smoothing) problem. The below equation is a general linear kernel regression function.

$y^* = \frac{\sum^N_{i=1}K(x^*,x_i)y_i}{\sum^N_{i=1}K(x^*,x_i)}$

Here $x_i$ is the i th training data input, $y_i$ is the i th training data output, K is a kernel function. $x^*$ is a query point, $y^*$ is the predicted output.

The Gaussian Kernel, also known as the Radial Base Function (RBF), is one of the most common kernels. It is expressed with the below equation.

$K(x^*,x_i)=\exp\left(-\frac{(x^*-x_i)^2}{2b^2}\right)$

Here, b is the length scale for the input space.

## Regression statistics

The assumption underlying this procedure is that the model can be approximated by a linear function.

$f(x_i,\boldsymbol\beta)\approx f^0+\sum_j J_{ij}\beta_j$

where $J_{ij}=\frac{\partial f(x_i,\boldsymbol\beta)}{\partial \beta_j}$. It follows from this that the least squares estimators are given by

$\hat{\boldsymbol{\beta}} \approx \mathbf { (J^TJ)^{-1}J^Ty}.$

The nonlinear regression statistics are computed and used as in linear regression statistics, but using J in place of X in the formulas. The linear approximation introduces bias into the statistics. Therefore more caution than usual is required in interpreting statistics derived from a nonlinear model.

## Ordinary and weighted least squares

The best-fit curve is often assumed to be that which minimizes the sum of squared residuals. This is the (ordinary) least squares (OLS) approach. However, in cases where the dependent variable does not have constant variance a sum of weighted squared residuals may be minimized; see weighted least squares. Each weight should ideally be equal to the reciprocal of the variance of the observation, but weights may be recomputed on each iteration, in an iteratively weighted least squares algorithm.

## Linearization

### Transformation

Some nonlinear regression problems can be moved to a linear domain by a suitable transformation of the model formulation.

For example, consider the nonlinear regression problem

$y = a e^{b x}U \,\!$

with parameters a and b and with multiplicative error term U. If we take the logarithm of both sides, this becomes

$\ln{(y)} = \ln{(a)} + b x + u, \,\!$

where u = log(U), suggesting estimation of the unknown parameters by a linear regression of ln(y) on x, a computation that does not require iterative optimization. However, use of a nonlinear transformation requires caution. The influences of the data values will change, as will the error structure of the model and the interpretation of any inferential results. These may not be desired effects. On the other hand, depending on what the largest source of error is, a nonlinear transformation may distribute your errors in a normal fashion, so the choice to perform a nonlinear transformation must be informed by modeling considerations.

For Michaelis–Menten kinetics, the linear Lineweaver–Burk plot

$\frac{1}{v} = \frac{1}{V_\max} + \frac{K_m}{V_{\max}[S]}$

of 1/v against 1/[S] has been much used. However, since it is very sensitive to data error and is strongly biased toward fitting the data in a particular range of the independent variable, [S], its use is strongly discouraged.

For error distributions that belong to the Exponential family, a link function may be used to transform the parameters under the Generalized linear model framework.

### Segmentation

Yield of mustard and soil salinity

The independent or explanatory variable (say X) can be split up into classes or segments and linear regression can be performed per segment. Segmented regression with confidence analysis may yield the result that the dependent or response variable (say Y) behaves differently in the various segments.[1]

The figure shows that the soil salinity (X) initially exerts no influence on the crop yield (Y) of mustard (colza), until a critical or threshold value (breakpoint), after which the yield is affected negatively.[2]