The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
The search session has expired. Please query the service again.
In this paper, we consider a multidimensional convolution model for which we provide adaptive anisotropic kernel estimators of a signal density measured with additive error. For this, we generalize Fan’s (
(3) (1991) 1257–1272) estimators to multidimensional setting and use a bandwidth selection device in the spirit of Goldenshluger and Lepski’s (
(3) (2011) 1608–1632) proposal for density estimation without noise. We consider first the pointwise setting and then,...
We show how to capture the gradient concentration of the solutions of Dirichlet-type problems subjected to large sources of order concentrated on an -neighborhood of a hypersurface of the domain. To this end we define the gradient Young-concentration measures generated by sequences of finite energy and establish a very simple characterization of these measures.
We show how to capture the gradient concentration of the solutions of Dirichlet-type
problems subjected to large sources of order concentrated on an -neighborhood of a hypersurface of the domain. To this end we define the
gradient Young-concentration measures generated by sequences of finite energy and establish a very simple
characterization of these measures.
Download Results (CSV)