Hello dear academics/researchers
I want to use a test for robustness after conducting KRLS test. When i was looking for alternative tests i found lpoly and localp (advanced version of lpoly). Local Polynomial Regression is nice to visualization of graphs. But i'm not sure it will be a valid test after conducting KRLS. I couldn't find any paper that help me. That's why i prefer to ask you dear academics/researchers. Thank you in advance
Metin Dogan Combining Kernel-based Regularized Least Squares (KRLS) with Local Polynomial Regression (LPR) can indeed be an interesting approach, but it largely depends on your research objectives and the type of robustness or validation you are seeking.
KRLS is a non-parametric regression method that excels in capturing complex, non-linear relationships in your data. On the other hand, LPR, especially with tools like lpoly or its advanced version localp, is often used for visualizing trends in the data or performing exploratory smoothing to understand local patterns.
If your goal is to use LPR as a complementary tool for robustness checks or graphing after KRLS, it can be valid, provided you are clear about the specific purpose:
Ultimately, while combining these methods is not inherently invalid, the validity will depend on your methodological rigor and how you communicate the intent behind using both.
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