Hi there,
I have a nonlinear regression model that there is an uncertainty about its input data. I want to calculate the standard error of model so that includes the uncertainty (or error) of input data. How can I do it? Can anyone help me? Thanks.
It should be noted that, the uncertain input data is dependent variable of the regression analysis.
you should attach your data or regression output, so that I can see and calculate the standard error in the non-linear regression model. The following example of how to calculate the error srandar
Can you say more about what you mean by the "uncertainty" of your dependent variable?
I would use a stochastic regression, such as Bayesian Model Averaging (BMA), to quantify the uncertainty in your overall data by generating multiple models not only one. Then, you select the less uncertain model based on Bayes Theorem and posterior probability.
Research Integrating Bayesian Model Averaging for Uncertainty Reducti...
Conference Paper Comparison of Permeability Estimation Models Through Bayesia...
Conference Paper Applied Bayesian Multivariate Geostatistical Algorithm for F...
See the attached Wikipedia page. That does not cover the whole of the problem, but is a good start.
I agree with Mudhafar above that Bayesian modeling with a modern tool such as JAGS or Stan is probably the easier way to model this case ; the priors issue can be an opportunity to use previous knowledge about your problem, or can be sort-of-avoided by using a standard "weakly informative prior" if you want to analyse your data as standing alone...
https://en.wikipedia.org/wiki/Errors-in-variables_models
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