Semiparametric theory and missing data / Anastasios A. Tsiatis.
By: Tsiatis, Anastasios A. (Anastasios Athanasios)
Material type: 
TextLanguage: English Series: Springer series in statisticsPublisher: New York : Springer, c2006Description: xvi, 383 p. : ill. ; 24 cmISBN: 9780387324487 (acidfree paper); 0387324488 (acidfree paper)Subject(s): Parameter estimation | Missing observations (Statistics)DDC classification: 519.544 LOC classification: QA276.8 | .T75 2006Online resources: Publisher description | WorldCat details | E-book Fulltext | Item type | Current location | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds |
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E-Book
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EWU Library E-book | Non-fiction | 519.544 TSS 2006 (Browse shelf) | Not for loan | ||||
Text
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EWU Library Reserve Section | Non-fiction | 519.544 TSS 2006 (Browse shelf) | C-1 | Not For Loan | 26629 |
Includes bibliographical references and index.
Table of contents Introduction to semiparametric models --
Hilbert Space for random vectors --
The geometry of influence functions --
Semiparametric models --
Examples of semiparametric models --
Models and methods for missing data --
Missing and coarsening at random for semiparametric models --
The nuisance tangent space and its orthogonal complement --
Augmented inverse probability weighted complete-case estimators --
Improving efficiency and double robustness with coarsened data --
Locally efficient estimators for coarsened-data semiparametric models --
Approximate methods for gaining efficiency --
Double-robust estimator of the average causal treatment effect --
Multiple imputation: a frequentist perspective.
Summary:
This book summarizes current knowledge of the theory of estimation for semiparametric models with missing data, applying modern methods to missing, censored, and coarsened data with the goal of Read more...
Applied Statistics
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