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Multivariate nonlinear regression model fitting

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Jorge on 6 Jul 2018
Edited: Anton Semechko on 6 Jul 2018
I apologize since I am new to matlab
I have built a multivariate model to describe experimental data and I am trying to set up a nonlinear regression fitting to extract parameters for the model.
The model has two dependent variables that depend nonlinearly on two independent variables The model has three parameters.
I found the mvregress function, but as I understand it, it is a multivariate linear regression, which does not apply to my problem.
Thank you in advance for any help

Accepted Answer

Anton Semechko
Anton Semechko on 6 Jul 2018
Edited: Anton Semechko on 6 Jul 2018
If the function you are trying to fit is linear in terms of model parameters, you can estimate these parameters using linear least squares ( 'lsqlin' documentation). If there is a nonlinear relashionship between model parameters and the function, use nonlinear least squares ( 'lsqnonlin' documentation). For example, F(x,y,c1,c2,c3)=c1*x^2 + c2*exp(y) + c3*cos(x-y), is nonlinear in terms of (x,y), but is a linear function of (c1,c2,c3) (i.e., model parameters).
Anton Semechko
Anton Semechko on 6 Jul 2018
Bootstraping is one option. Another option is to use jack-knife (i.e., leave-one-out cross-validation). Although if you have a large dataset, boostraping may be a more effective option (from computational perspective).

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