Nonparametric Estimation under Shape Constraints: Estimators, Algorithms and Asymptotics

Piet Groeneboom, Geurt Jongbloed

ISBN 10: 0521864011 / ISBN 13: 9780521864015

Published by Cambridge University Press, 2014

About this Item

This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Among the topics treated are current status and interval censoring models, competing risk models, and deconvolution. Methods of order restricted inference are used in computing maximum likelihood estimators and developing distribution theory for inverse problems of this type. The authors have been active in developing these tools and present the state of the art and the open problems in the field. The earlier chapters provide an introduction to the subject, while the later chapters are written with graduate students and researchers in mathematical statistics in mind. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.

Seller Inventory # 0075680

Bibliographic Details

• Title: Nonparametric Estimation under Shape Constraints: Estimators, Algorithms and Asymptotics
• Publisher: Cambridge University Press
• Publication Date: 2014
• Language: English
• ISBN 10: 0521864011
• ISBN 13: 9780521864015
• Binding: Hardback
• Condition: New

About this title

Synopsis

This book treats the latest developments in the theory of order-restricted inference, with special attention to nonparametric methods and algorithmic aspects. Among the topics treated are current status and interval censoring models, competing risk models, and deconvolution. Methods of order restricted inference are used in computing maximum likelihood estimators and developing distribution theory for inverse problems of this type. The authors have been active in developing these tools and present the state of the art and the open problems in the field. The earlier chapters provide an introduction to the subject, while the later chapters are written with graduate students and researchers in mathematical statistics in mind. Each chapter ends with a set of exercises of varying difficulty. The theory is illustrated with the analysis of real-life data, which are mostly medical in nature.

About the Authors

Piet Groeneboom is Professor Emeritus of Statistics at Delft University of Technology, The Netherlands.

Geurt Jongbloed is Professor of Statistics at Delft University of Technology, The Netherlands.