Block thresholding and wavelet estimation for nonequispaced samples

For samples with the design points occurring as a Poisson process or having a uniform distribution, the wavelet method of block thresholding can be applied directly to the data as though it was equispaced without sacrificing adaptivity or optimality. When the underlying true function is in certain Besov and Holder classes, the resulting estimator achieves the minimax rate of convergence. Simulation results are examined. (C) 2002 Elsevier B.V. All rights reserved.