Approximate quasilikelihood estimation in models with surrogate predictors /

Bibliographic Details
Main Authors:	Carroll, Raymond J. (Author), Stefanski, Leonard A. (Author)
Corporate Authors:	National Institutes of Health (U.S.) (sponsoring body.), National Science Foundation (U.S.) (sponsoring body.), Harvard School of Public Health (sponsoring body.), SIAM Institute for Mathematics and Society (sponsoring body.), United States. Environmental Protection Agency (sponsoring body.)
Format:	Book
Language:	English
Published:	College Station, Texas : Department of Statistics, Texas A & M University, [1990]
Series:	Technical report (Texas A & M University. Department of Statistics) ; no. 87.
Subjects:	Errors-in-variables models. Error analysis (Mathematics) Estimation theory > Asymptotic theory. Mathematical statistics. Berkson error Measurement error Quasilikelihood Reliability data Validation data

Description
Abstract:	We consider quasilikelihood estimation with estimated parameters in the variance function when some of the predictors are measured with error. We review and extend four approaches to estimation in this problem, all of them based on small measurement error approximations. A taxonomy of the data sets likely to be available in measurement error studies is developed. An asymptotic theory based on this taxonomy is obtained and includes measurement error and Berkson error models as special cases.
Item Description:	Offprint: Journal of the American Statistical Association. Funding information taken from leaf ii.
Physical Description:	23 leaves, 2 unnumbered leaves : illustrations ; 28 cm
Bibliography:	Includes bibliographical references (leaves 21-22).