Estimation in the convolution structure density model. Part II: adaptation over the scale of anisotropic classes

This paper continues the research started in \cite{LW16}. In the framework of the convolution structure density model on \(\bR^d\), we address the problem of adaptive minimax estimation with \(\bL_p\)--loss over the scale of anisotropic Nikol'skii classes. We fully characterize the behavior of the minimax risk for different relationships between regularity parameters and norm indexes in the definitions of the functional class and of the risk. In particular, we show that the boundedness of the function to be estimated leads to an essential improvement of the asymptotic of the minimax risk. We prove that the selection rule proposed in Part I leads to the construction of an optimally or nearly optimally (up to logarithmic factor) adaptive estimator.