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Eggermont, Paul; Lariccia, Vincent - Maximum Penalized Likelihood Estimation - 9780387952680 - V9780387952680
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Maximum Penalized Likelihood Estimation

€ 241.96
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Description for Maximum Penalized Likelihood Estimation Hardback. Deals with parametric and nonparametric density estimation from the maximum (penalized) likelihood point of view, including estimation under constraints such as unimodality and log-concavity. This book focuses on convexity and convex optimization, as applied to maximum penalized likelihood estimation. Series: Springer Series in Statistics. Num Pages: 530 pages, biography. BIC Classification: PBT. Category: (P) Professional & Vocational; (UP) Postgraduate, Research & Scholarly. Dimension: 234 x 156 x 28. Weight in Grams: 917.
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 ... Read more

Product Details

Format
Hardback
Publication date
2001
Publisher
Springer-Verlag New York Inc. United States
Number of pages
530
Condition
New
Series
Springer Series in Statistics
Number of Pages
512
Place of Publication
New York, NY, United States
ISBN
9780387952680
SKU
V9780387952680
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15

Reviews for Maximum Penalized Likelihood Estimation
From the reviews: "…A highly readable and appealing book…In a world of dry prose, this book is a refreshing change…The book is enjoyable to read, which alone merits praise." Journal of the American Statistical Association "This is a theoretical work, but the authors always keep the practical aspect in mind. Algorithmic issues are treated with great ... Read more

Goodreads reviews for Maximum Penalized Likelihood Estimation


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