In simple linear regression, quadratic programming can be used to solve the problem where for least squares, the objective is
$$\begin{array}{ll} \min & Q(\beta)=(Y-X \beta)^{\prime}(Y-X \beta) \ \text { s.t. } & A \beta \geq C \ & \beta \geq 0 \end{array}$$
The notation should be fairly self-explanatory.
However, for nonlinear regression, things are more complicated. For example, the MichaelisMenten model is multivariate, given by $f(x, \beta)=\beta_{1} x /\left(\beta_{2}+x\right)$. It is possible to transform any nonlinear model to a linear one, but there is an element of risk as the errors are altered.
Is there any literature that provides a procedure on how to tackle this type of regression?

首先网上有不错的资料可以参考：NEOS有一个关于非线性最小二乘的不错的网页。它包含非线性最小二乘法的几个经典（即，不是现在的最佳结果，但作为用来理解非线性优化的精髓很好）参考。

real analysis代写analysis 2, analysis 3请认准UprivateTA™. UprivateTA™为您的留学生涯保驾护航。

# 概率论代考

## 离散数学代写

Categories: 数学代写统计