Nonlinear Regression: Residual Analysis
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Hi,
when performing a residual analysis of a classic linear regression model, the residuals typically have to fulfill three requirements:
1) Normal distribution
2) Constant Variance (Homoscedasticity)
3) Freedom of Autocorrelation
However, few sources can be found about residual analysis in nonlinear regression (especially if robust methods such as bisquare or LAR are applied). Does anyone know if the requirements to the residuals remain the same?
Thanks
Vince
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Krispy Scripts
2016-12-1
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My understanding is that non-linear regression is needed when such things as normal distribution, homoscedasticity, and autocorrelation are not met, that is that there is some correlation among dimensions that is not linear. I do not know as to any stated residuals that are required or necessary for non-linear regression. However, those performing non-linear regression normally plot the residual plot, which in non-linear regression tends to show hyperbolic or other abnormal like distributions instead of the normal spread around the horizontal of a residual plot.
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2016-12-1
2016-12-1
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