Maximum Penalized Likelihood Estimation (Hardcover)

P.P.B. Eggermont

ISBN 10: 0387952683 / ISBN 13: 9780387952680

Edité par Springer-Verlag New York Inc., New York, NY, 2001

A propos de cet article

Hardcover. This book is intended for graduate students in statistics and industrial mathematics, as well as researchers and practitioners in the field. We cover 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. We also use (and develop from an elementary view point) discrete parameter submartingales and exponential inequalities. 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. Some theoretical topics that appear in textbook form for the first time are definitive treatments of I.J. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

N° de réf. du vendeur 9780387952680

Détails bibliographiques

• Titre: Maximum Penalized Likelihood Estimation (Hardcover)
• Éditeur: Springer-Verlag New York Inc., New York, NY
• Date de parution: 2001
• Langue: anglais
• ISBN à 10 chiffres: 0387952683
• ISBN à 13 chiffres: 9780387952680
• Reliure: Hardcover
• État de l'article: new

A propos de ce titre

Synopsis

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