Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work is motivated by the need to deal with models with high-dimensional input spaces of real variables. One way to tackle high-dimensional problems is to identify interaction or non-interaction among input parameters.  propose a new variance-based sensitivity interaction index that can detect and quantify interactions among the input variables of mathematical functions and computer simulations. The computation is very similar to first-order sensitivity indices by Sobol’. The proposed interaction index can quantify the relative importance of input variables in interaction. Furthermore, detection of non-interaction for screening can be done with as low as   4n+2     4  n  +  2        function evaluations, where   n     n        is the number of input variables. Using the interaction indices based on heteroscedasticity, the original function may be decomposed into a set of lor dimensional functions which may then be analyzed separately.
['Keiichi Ito', 'Ivo Couckuyt', 'Silvia Poles', 'Tom Dhaene']
I am writing a research paper on a idea Variance-based interaction index measuring heteroscedasticity
[{"from": "human", "value": "I am writing a research paper on a idea Variance-based interaction index measuring heteroscedasticity"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: This work is motivated by the need to deal with models with high-dimensional input spaces of real variables. One way to tackle high-dimensional problems is to identify interaction or non-interaction among input parameters.  propose a new variance-based sensitivity interaction index that can detect and quantify interactions among the input variables of mathematical functions and computer simulations. The computation is very similar to first-order sensitivity indices by Sobol\u2019. The proposed interaction index can quantify the relative importance of input variables in interaction. Furthermore, detection of non-interaction for screening can be done with as low as   4n+2     4  n  +  2        function evaluations, where   n     n        is the number of input variables. Using the interaction indices based on heteroscedasticity, the original function may be decomposed into a set of lor dimensional functions which may then be analyzed separately."}]