This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.
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This book summarizes current knowledge regarding the theory of estimation for semiparametric models with missing data, in an organized and comprehensive manner. It starts with the study of semiparametric methods when there are no missing data. The description of the theory of estimation for semiparametric models is both rigorous and intuitive, relying on geometric ideas to reinforce the intuition and understanding of the theory. These methods are then applied to problems with missing, censored, and coarsened data with the goal of deriving estimators that are as robust and efficient as possible.
Imprint | Springer-Verlag New York |
Country of origin | United States |
Series | Springer Series in Statistics |
Release date | June 2006 |
Availability | Expected to ship within 10 - 15 working days |
First published | 2006 |
Authors | Anastasios Tsiatis |
Dimensions | 235 x 155 x 31mm (L x W x T) |
Format | Hardcover |
Pages | 388 |
Edition | 2006 ed. |
ISBN-13 | 978-0-387-32448-7 |
Barcode | 9780387324487 |
Categories | |
LSN | 0-387-32448-8 |