The Annals of Statistics

The Equivalence of Weak, Strong and Complete Convergence in $L_1$ for Kernel Density Estimates

Luc Devroye

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Abstract

Let $f$ be a density on $R^d$, and let $f_n$ be the kernel estimate of $f$, $f_n(x) = (nh^d)^{-1} \sum^n_{i=1} K((x - X_i)/h)$ where $h = h_n$ is a sequence of positive numbers, and $K$ is an absolutely integrable function with $\int K(x) dx = 1$. Let $J_n = \int |f_n(x) - f(x)| dx$. We show that when $\lim_nh = 0$ and $\lim_nnh^d = \infty$, then for every $\varepsilon > 0$ there exist constants $r, n_0 > 0$ such that $P(J_n \geq \varepsilon) \leq \exp(-rn), n \geq n_0$. Also, when $J_n \rightarrow 0$ in probability as $n \rightarrow \infty$ and $K$ is a density, then $\lim_nh = 0$ and $\lim_nnh^d = \infty$.

Article information

Source
Ann. Statist., Volume 11, Number 3 (1983), 896-904.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176346255

Digital Object Identifier
doi:10.1214/aos/1176346255

Mathematical Reviews number (MathSciNet)
MR707939

Zentralblatt MATH identifier
0521.62033

JSTOR
links.jstor.org

Subjects
Primary: 60F15: Strong theorems
Secondary: 62G05: Estimation

Keywords
Nonparametric density estimation $L_1$ convergence kernel estimate strong consistency

Citation

Devroye, Luc. The Equivalence of Weak, Strong and Complete Convergence in $L_1$ for Kernel Density Estimates. Ann. Statist. 11 (1983), no. 3, 896--904. doi:10.1214/aos/1176346255. https://projecteuclid.org/euclid.aos/1176346255


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