This book is intended for graduate students in statistics and industrial
mathematics, as well as researchers and practitioners in the field. It
covers both theory and practice of nonparametric estimation. The text is
novel in its use of maximum penalized likelihood estimation, and the
theory of convex minimization problems (fully developed in the text) to
obtain convergence rates. A substantial effort has been made to discuss
computational details, and to include simulation studies and analyses of
some classical data sets using fully automatic (data driven) procedures.