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A nearest neighbor estimate of the residual variance. (English) Zbl 1395.62088
Summary: We study the problem of estimating the smallest achievable mean-squared error in regression function estimation. The problem is equivalent to estimating the second moment of the regression function of \(Y\) on \(X\in\mathbb R^d\). We introduce a nearest-neighbor-based estimate and obtain a normal limit law for the estimate when \(X\) has an absolutely continuous distribution, without any condition on the density. We also compute the asymptotic variance explicitly and derive a non-asymptotic bound on the variance that does not depend on the dimension \(d\). The asymptotic variance does not depend on the smoothness of the density of \(X\) or of the regression function. A non-asymptotic exponential concentration inequality is also proved. We illustrate the use of the new estimate through testing whether a component of the vector \(X\) carries information for predicting \(Y\).

MSC:
62G08 Nonparametric regression and quantile regression
62G20 Asymptotic properties of nonparametric inference
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