Robust linear regression in replicated measurement error models /

Bibliographic Details
Main Authors:	Carroll, Raymond J. (Author), Eltinge, John L. (Author), Ruppert, David, 1948- (Author)
Corporate Authors:	National Science Foundation (U.S.) (sponsoring body.), Cornell University. Mathematical Sciences Institute (sponsoring body.), National Institutes of Health (U.S.) (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. 128.
Subjects:	Errors-in-variables models. Regression analysis. Error analysis (Mathematics) Estimation theory > Asymptotic theory. Mathematical statistics. Bounded influence estimators Case delegation diagnostics Leverage Mallows estimates

Description
Abstract:	We propose robust and bounded influence methods for linear regression when some of the predictors are measured with error. We address the important special case that the surrogate predictors are replicated. The robust methods proposed are variants of the so-called Mallows class of estimates. The resulting estimators are easily computed and have a simple asymptotic theory. An example is used to illustrate the results.
Item Description:	"The work of Carroll and Eltinge was supported by the National Institutes of Health...Ruppert's work was partially supported by the Mathematical Sciences Institute at Cornell University and by a grant from the National Science Foundation"--Leaf 12.
Physical Description:	13 leaves, 7 unnumbered leaves : illustrations ; 28 cm
Bibliography:	Includes bibliographical references (leaves 12-13).