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Approaches to estimation with errors in predictors /

We provide an overview of some approaches to estimation in generalized linear models when predictors are measured with error. These approaches include likelihood, small error, semiparametric and dimension reduction methods.

Bibliographic Details
Main Author: Carroll, Raymond J. (Author)
Corporate Author: National Institutes of Health (U.S.) (sponsoring body.)
Format: Book
Language:English
Published: College Station, Texas : Department of Statistics, Texas A & M University, 1992.
Series:Technical report (Texas A & M University. Department of Statistics) ; no. 167.
Subjects:
Estimation theory.
Linear models (Statistics)
Errors-in-variables models.
Dimension reduction (Statistics)
Regression analysis.
Mathematical statistics.
Description
Summary:We provide an overview of some approaches to estimation in generalized linear models when predictors are measured with error. These approaches include likelihood, small error, semiparametric and dimension reduction methods.
Item Description:Funding information taken from leaf 6.
Physical Description:8 unnumbered leaves ; 28 cm
Bibliography:Includes bibliographical references (leaves 6-8).