Maximum Penalized Likelihood Estimation

This reference book is intended for graduate students and researchers in statistics, industrial and engineering mathematics, and operations research.

Reference Book intended for grad students and researchers in statistics, industrial and Engineering mathematics, and operations research Convexity and convex optimization receive special attention Includes supplementary material: sn.pub/extras

Zusammenfassung
Good's roughness penalization, monotone and unimodal density estimation, asymptotic optimality of generalized cross validation for spline smoothing and analogous methods for ill-posed least squares problems, and convergence proofs of EM algorithms for random sampling problems.

Inhalt
Parametric Maximum Likelihood Estimation.- Parametric Maximum Likelihood Estimation in Action.- Kernel Density Estimation.- Maximum Likelihood Density Estimation.- Monotone and Unimodal Densities.- Choosing the Smoothing Parameter.- Nonparametric Density Estimation in Action.- Convex Minimization in Finite Dimensional Spaces.- Convex Minimization in Infinite Dimensional Spaces.- Convexity in Action.