As far as my understanding goes, the LLP model works by assuming linear relationship between variables for every small kernel. Hence for n observations there will be n different linear models.
If this is the case won’t it be difficult to quantify the significance of a parameter/ variable on the model outcome.
What I want is:
If there are x independent variables in LLP regression leading to an outcome, is there a way to assign a significance to the input variables such that variable 1 is more important than variable 2 and can ultimately be dropped from the equation!