Adaptive nonparametric estimation of a component density in a two-class mixture model
Báo cáo viênr: TS. Hoàng Văn Hà (ĐHKHTN-ĐHQGHCM)

Thời gian: 14h Thứ 5, ngày 26/05/2022

Link online Zoom: 836 3396 5365

Passcode: 016509

Tóm tắt: A two-class mixture model, where the density of one of the components is known, is considered. We address the issue of the nonparametric adaptive estimation of the unknown probability density of the second component. We propose a randomly weighted kernel estimator with a fully data-driven bandwidth selection method, in the spirit of the Goldenshluger and Lepski method. An oracletype inequality for the pointwise quadratic risk is derived as well as convergence rates over H¨older smoothness classes. The theoretical results are illustrated by numerical simulations. This is a joint work with Gaelle Chagny, Antoine Channarond and Angelina Roche.

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