The Hilbert Kernal regression estimate
Journal of Multivariate Analysis
Wavelet neural network predication for epidemic outbreak
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
An affine invariant k-nearest neighbor regression estimate
Journal of Multivariate Analysis
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Let X be an Rd-valued random variable with unknown density f. Let X1 ...., Xn be i.i.d, random variables drawn from f. The objective is to estimate f(x), where x = (x1 ...., xd), We study the pointwise convergence of two new density estimates, the Hilbert product kernel estimate d!/n Σi=1nΣj=1d1/2log n|xjXij|, where Xt = (Xi1 ..., Xid), and the Hilbert k-nearest neighbor estimate k(d-1)!/2dnlogd-1(n/(k(d-1)!)) Πj=1d|xj-X(k)j|, where X(k) = (X(k)1, ..., X(k)d), and X(k) is the kth nearest neighbor of x when points are ordered by increasing values of the product Πj=1d |xj-X(k)j|, and k = o(log n), k → ∞. The auxiliary results needed permit us to formulate universal consistency results (pointwise and in L1) for product kernel estimates with different window widths for each coordinate, and for rectangular partitioning and tree estimates. In particular, we show that locally adapted smoothing factors for product kernel estimates may make the kernel estimate inconsistent even under standard conditions on the bandwidths.