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Some ideas in nonparametric estimation

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The talk will be in two parts. In part 1, I will discuss an extension of “Wild Binary Segmentation” for the detection of general features in data and the estimation of curves. In this approach, simple features are fitted over a large collection of subsets of the data, and the results aggregated in a fast hierarchical fashion.

In part 2, I will outline the construction of a class of algorithms for fast bottom-up processing of data living on graphs (which includes curves and images), and describe how the structure of the algorithm enforces desirable theoretical behaviour.

Both parts concern work in progress, but the nearest related references are

Wild Binary Segmentation for multiple change-point detection. P. Fryzlewicz (2014). Annals of Statistics, 42, 2243-2281.

SHAH : SHape-Adaptive Haar wavelets for image denoising and classification. P. Fryzlewicz and C. Timmermans (2014). Under revision.

This talk is part of the Statistics series.

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