Optimal rates for plug-in estimators of density level sets

Details Optimal rates for plug-in estimators of density level sets Optimal rates for plug-in estimators of density level sets

2006-11-15

arxiv.org/abs/math/0611473v4

Bernoulli 2009, Vol. 15, No. 4, 1154-1178

Mathematics

Statistics Theory

Published in at http://dx.doi.org/10.3150/09-BEJ184 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statisti

Scientific paper

10.3150/09-BEJ184

In the context of density level set estimation, we study the convergence of general plug-in methods under two main assumptions on the density for a given level $\lambda$. More precisely, it is assumed that the density (i) is smooth in a neighborhood of $\lambda$ and (ii) has $\gamma$-exponent at level $\lambda$. Condition (i) ensures that the density can be estimated at a standard nonparametric rate and condition (ii) is similar to Tsybakov's margin assumption which is stated for the classification framework. Under these assumptions, we derive optimal rates of convergence for plug-in estimators. Explicit convergence rates are given for plug-in estimators based on kernel density estimators when the underlying measure is the Lebesgue measure. Lower bounds proving optimality of the rates in a minimax sense when the density is H\"older smooth are also provided.

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