This book deals with parametric and nonparametric density estimation
from the maximum (penalized) likelihood point of view, including
estimation under constraints. The focal points are existence and
uniqueness of the estimators, almost sure convergence rates for the L1
error, and data-driven smoothing parameter selection methods, including
their practical performance. The reader will gain insight into technical
tools from probability theory and applied mathematics.