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Asymptotic normality and strong consistency of kernel regression estimation in q-calculus (2503.07088v1)
Published 10 Mar 2025 in math.ST and stat.TH
Abstract: We construct a family of estimators for a regression function based on a sample following a qdistribution. Our approach is nonparametric, using kernel methods built from operations that leverage the properties of q-calculus. Furthermore, under appropriate assumptions, we establish the weak convergence and strong consistency of this family of estimators.
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