Semiparametric theory and missing data / Anastasios A. Tsiatis.

By: Tsiatis, Anastasios A. (Anastasios Athanasios)Material type: Text

TextLanguage: English Series: Springer series in statisticsPublication details: New York : Springer, c2006. Description: 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

Contents:

TOC 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: 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...

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Holdings
Item type	Current library	Collection	Call number	Copy number	Status	Date due	Barcode	Item holds
E-Book	Dr. S. R. Lasker Library, EWU E-book	Non-fiction	519.544 TSS 2006 (Browse shelf(Opens below))		Not for loan
Text	Dr. S. R. Lasker Library, EWU Reserve Section	Non-fiction	519.544 TSS 2006 (Browse shelf(Opens below))	C-1	Not For Loan		26629

Total holds: 0

Includes bibliographical references and index.

TOC 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...

Saifun Momota

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