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.
General
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