Buch, Englisch, 512 Seiten, Format (B × H): 161 mm x 241 mm, Gewicht: 2020 g
Volume I: Density Estimation
Buch, Englisch, 512 Seiten, Format (B × H): 161 mm x 241 mm, Gewicht: 2020 g
Reihe: Springer Series in Statistics
ISBN: 978-0-387-95268-0
Verlag: Springer
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.
Zielgruppe
Research
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
- Mathematik | Informatik Mathematik Operations Research
Weitere Infos & Material
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.
