
Adaptive optimal kernel density estimation for directional data
We focus on the nonparametric density estimation problem with directiona...
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Nonparametric method for space conditional density estimation in moderately large dimensions
In this paper, we consider the problem of estimating a conditional densi...
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Minimax Optimal Conditional Density Estimation under Total Variation Smoothness
This paper studies the minimax rate of nonparametric conditional density...
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Estimation of the Global Mode of a Density: Minimaxity, Adaptation, and Computational Complexity
We consider the estimation of the global mode of a density under some de...
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Density estimation on an unknown submanifold
We investigate density estimation from a nsample in the Euclidean space...
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Fast Nonparametric Conditional Density Estimation
Conditional density estimation generalizes regression by modeling a full...
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Fully adaptive densitybased clustering
The clusters of a distribution are often defined by the connected compon...
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Adaptive greedy algorithm for moderately large dimensions in kernel conditional density estimation
This paper studies the estimation of the conditional density f (x, ×) of Y i given X i = x, from the observation of an i.i.d. sample (X i , Y i) ∈ R d , i = 1,. .. , n. We assume that f depends only on r unknown components with typically r d. We provide an adaptive fullynonparametric strategy based on kernel rules to estimate f. To select the bandwidth of our kernel rule, we propose a new fast iterative algorithm inspired by the Rodeo algorithm (Wasserman and Lafferty (2006)) to detect the sparsity structure of f. More precisely, in the minimax setting, our pointwise estimator, which is adaptive to both the regularity and the sparsity, achieves the quasioptimal rate of convergence. Its computational complexity is only O(dn log n).
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