By László Györfi, Michael Kohler, Adam Krzyzak, Harro Walk

This booklet presents a scientific in-depth research of nonparametric regression with random layout. It covers just about all identified estimates. The emphasis is on distribution-free houses of the estimates.

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**Extra resources for A Distribution-Free Theory of Nonparametric Regression (Springer Series in Statistics)**

**Sample text**

Gj,k (Xil )) + (Ni1 , . . , Nil ), while {Y1 , . . , Yn } \ {Yi1 , . . , Yil } depends only on C \ {Cj,k } and on Xr ’s and Nr ’s with r ∈ {i1 , . . , il }, and therefore is independent of Cj,k given X1 , . . , Xn . Now conditioning on X1 , . . , Xn , the error of the conditional Bayes decision for Cj,k based on (Y1 , . . , Yn ) depends only on (Yi1 , . . 2 implies ⎛ ⎞ P{C¯n,j,k = Cj,k |X1 , . . , Xn } = Φ ⎝− l r=1 2 (X )⎠ gj,k ir 48 3. Lower Bounds ⎛ n Φ ⎝− = ⎞ 2 (X )⎠ . gj,k i i=1 √ Since Φ(− x) is convex, by Jensen’s inequality P{C¯n,j,k = Cj,k } = E{P{C¯n,j,k = Cj,k |X1 , .

2 implies ⎛ ⎞ P{C¯n,j,k = Cj,k |X1 , . . , Xn } = Φ ⎝− l r=1 2 (X )⎠ gj,k ir 48 3. Lower Bounds ⎛ n Φ ⎝− = ⎞ 2 (X )⎠ . gj,k i i=1 √ Since Φ(− x) is convex, by Jensen’s inequality P{C¯n,j,k = Cj,k } = E{P{C¯n,j,k = Cj,k |X1 , . . 5) j:npj2p+d ≤1 where K1 = Φ − 1 2 g 2 (x) dx d−1 . Since bn and an tend to zero we can take a subsequence {nt }t∈N of {n}n∈N with bnt ≤ 2−t and ≤ 2−t .

Y is noiseless, the rate of convergence of any estimate can be arbitrarily slow. 32 3. 1. Let {an } be a sequence of positive numbers converging to zero. For every sequence of regression estimates, there exists a distribution of (X, Y ), such that X is uniformly distributed on [0, 1], Y = m(X), m is ±1 valued, and lim sup n→∞ E mn − m an 2 ≥ 1. Proof. Without loss of generality we assume that 1/4 ≥ a1 ≥ a2 ≥ · · · > 0, otherwise replace an by min{1/4, a1 , . . , an }. Let {pj } be a probability distribution and let P = {Aj } be a partition of [0, 1] such that Aj is an interval of length pj .