I'm a non-statistician trying to do a nonparametric kernel regression (1 input variable, 1 output variable) using Python, with the purpose of using it for prediction. My samples have weights, but I did not find any Python packages that support kernel regression with sample weights.
Being the naive person that I am, I'm thinking that maybe I can fake/approximate the weights by repeating each input sample many times (equal to its sample weight rounded to the nearest integer). Is that valid? Would that work? If some things will work correctly (e.g. using the result to predict mean) but other things will break (e.g. using the result to predict variance), please let me know which things will work with this naive plan.
I'm not a statistician, so mathematical/theoretical answers will probably not be understood by me. I'm hoping to get some simple answer so I can make a decision of whether to follow through with this naive plan or not. Thanks.