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On the layered nearest neighbour estimate, the bagged nearest neighbour estimate and the random forest method in regression and classification. (English) Zbl 1198.62048
Summary: Let \(X_1, \dots , X_n\) be identically distributed random vectors in \(\mathbb R^d\), independently drawn according to some probability density. An observation \(X_i\) is said to be a layered nearest neighbour (LNN) of a point \(x\) if the hyperrectangle defined by \(x\) and \(X_i\) contains no other data points. We first establish consistency results on \(L_n(x)\), the number of LNN of \(x\). Then, given a sample \((X, Y), (X_1, Y_1), \dots , (X_n, Y_n)\) of independent identically distributed random vectors from \(\mathbb R^d \times \mathbb R\), one may estimate the regression function \(r(x) = \mathbb E[Y|X = x]\) by the LNN estimate \(r_n(x)\), defined as an average over the \(Y_i\)’s corresponding to those \(X_i\) which are LNN of \(x\). Under mild conditions on \(r\), we establish the consistency of \(\mathbb E|r_n(x) -r(x)|^p\) towards 0 as \(n \rightarrow \infty \), for almost all \(x\) and all \(p\geq 1\), and discuss the links between \(r_n\) and the random forest estimates of L. Breiman [Mach. Learn. 45, No. 1, 5–32 (2001; Zbl 1007.68152)]. We finally show the universal consistency of the bagged (bootstrap-aggregated) nearest neighbour method for regression and classification.

62H12 Estimation in multivariate analysis
62G20 Asymptotic properties of nonparametric inference
62G08 Nonparametric regression and quantile regression
62H30 Classification and discrimination; cluster analysis (statistical aspects)
Full Text: DOI
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