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Stochastic Processes and their Applications
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Stochastic Processes and their Applications
Article . 2016 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2015
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A law of the iterated logarithm for Grenander’s estimator

A law of the iterated logarithm for Grenander's estimator
Authors: Dümbgen, Lutz; Wellner, Jon A.; Wolff, Malcolm;

A law of the iterated logarithm for Grenander’s estimator

Abstract

In this note we prove the following law of the iterated logarithm for the Grenander estimator of a monotone decreasing density: If $f(t_0) > 0$, $f'(t_0) < 0$, and $f'$ is continuous in a neighborhood of $t_0$, then \begin{eqnarray*} \limsup_{n\rightarrow \infty} \left ( \frac{n}{2\log \log n} \right )^{1/3} ( \widehat{f}_n (t_0 ) - f(t_0) ) = \left| f(t_0) f'(t_0)/2 \right|^{1/3} 2M \end{eqnarray*} almost surely where $ M \equiv \sup_{g \in {\cal G}} T_g = (3/4)^{1/3}$ and $ T_g \equiv \mbox{argmax}_u \{ g(u) - u^2 \} $; here ${\cal G}$ is the two-sided Strassen limit set on $R$. The proof relies on laws of the iterated logarithm for local empirical processes, Groeneboom's switching relation, and properties of Strassen's limit set analogous to distributional properties of Brownian motion.

11 pages, 3 figures

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Keywords

Strassen limit set, local empirical process, 60F15, 60F17, 62E20, 62F12, 62G20, Strong limit theorems, Functional limit theorems; invariance principles, Asymptotic distribution theory in statistics, Mathematics - Statistics Theory, Statistics Theory (math.ST), strong invariance theorem, Asymptotic properties of nonparametric inference, FOS: Mathematics, law of iterated logarithm, monotone density, Asymptotic properties of parametric estimators, Grenander estimator

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5
Average
Average
Average
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