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- W2065809680 abstract "To estimate the density f of a conditional expectation μ(Z) = E[X|Z], Steckley and Henderson (2003) sample independent copies Z 1 ,…,Z m ; then, conditional on Z i , they sample n independent samples of X, and their sample mean X i is an approximate sample of μ(Z i ). For a kernel density estimate f of f based on such samples and a bandwidth (smoothing parameter) h, they consider the mean integrated squared error (MISE), ∫(f(x)−f(x))2dx, and find rates of convergence of m, n and h that optimize the rate of convergence of MISE to zero. Inspired by the cross-validation approach in classical density estimation, we develop an estimate of MISE (up to a constant) and select the h that minimizes this estimate. While a convergence analysis is lacking, numerical results suggest that our method is promising." @default.
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- W2065809680 date "2011-12-11" @default.
- W2065809680 modified "2023-09-28" @default.
- W2065809680 title "A cross-validation approach to bandwidth selection for a kernel-based estimate of the density of a conditional expectation" @default.
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- W2065809680 doi "https://doi.org/10.5555/2431518.2431567" @default.
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