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Bandwidth selection in clustering with errors in variables

Abstract : We consider the problem of clustering when we observe a corrupted sample. We test two bandwidth selection methods. It allows to deal with the isotropic and the anisotropic problem of selecting the bandwidth of a deconvolution kernel. These methods are based on Lepski's type procedure. The first method compares empirical risks associated with different bandwidths by using ICI (Intersection of Confidence Intervals) rule whereas the second one computes the gradient of the empirical risk and allows us to construct an anisotropic data-driven bandwidth. Numerical experiments are proposed to illustrate the efficiency of the methods.
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Contributor : Sébastien Loustau Connect in order to contact the contributor
Submitted on : Thursday, November 6, 2014 - 4:50:48 PM
Last modification on : Wednesday, October 20, 2021 - 3:18:53 AM
Long-term archiving on: : Saturday, February 7, 2015 - 10:05:19 AM


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  • HAL Id : hal-01060476, version 1



Sébastien Loustau, Simon Souchet. Bandwidth selection in clustering with errors in variables. 2014. ⟨hal-01060476⟩



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