Sobol method vs OAT approach I have used one at a time (OAT) approach in my model for sensitivity analysis. Which gives me elementry effects and variances for the input parameters, helping me to eliminate the less influencial parameters. OAT is a local sensitivity analysis approach.
I now want to use Sobol analysis (global method) in my model on the parameters which I have selected from OAT. Sobol method is a variance based method which will give me the interaction effects in terms of sensitivity indices. 
But I am unsure what exactly I will get from Sobol method or how will it be different from OAT method. Hence I want to have a reasoning before using the method. Can we compare both the methods? Can anyone please come up with 


*

*some comparing points between these 2 methods and 

*advantages/uses of Sobol method in general? 

 A: OAT is a local method, meaning that you consider one point of your input space, and you then change its components/features one at a time. It gives you information on the sensitivity of your model on that specific point not on the entire space.
On the contrary, Sobol analysis is global method. You can have 


*

*the global importance of the input features on the compact space you consider (with 1st order indices)

*the importance of the interactions of input features between each others (with higher order indices and total indices), which you cannot have at all with OAT. 


There is a particular type of OAT method called Morris which consists in repeating OAT on multiple points of the input space, so that from these multiple local sensitivity analyses, you can get a global analysis. This method is however not as precise as Sobol indices (no way to differentiate between interaction and non-linear effect! for example) and is most of the time only advised if you want to do screening, i.e. removing the really non-important features as a first step. 
