Nonparametric estimation of optimal performance criteria in quality engineering /

Bibliographic Details
Main Authors:	Carroll, Raymond J. (Author), Hall, Peter, 1951-2016 (Author)
Corporate Author:	United States. Air Force. Office of Scientific Research (sponsoring body.)
Format:	Book
Language:	English
Published:	College Station, Texas : Department of Statistics, Texas A & M University, [1989]
Series:	Technical report (Texas A & M University. Department of Statistics) ; no. 65.
Subjects:	Nonparametric statistics. Quality control > Statistical methods. Taguchi methods (Quality control) Regression analysis. Convergence. Estimation theory. Mathematical optimization. Mathematical statistics. Statistics > Nonparametric inference > Nonparametric regression. Statistics > Nonparametric inference > Estimation. Statistics > Nonparametric inference > Asymptotic properties. Variance function estimation Performance measure independent of adjustment (PERMIA)

Description
Abstract:	Box (1987) and Leon et al. (1987) discuss the problem of closeness to target in quality engineering. If the mean response f(x, z) depends on (x, z), the variance function is a PERMIA if it is g(z), i.e., depends only on z. The goal is to find (x sub 0, z sub 0) which minimizes variance while achieving a target mean value. We pose and answer the question: for given smoothness assumptions about f and g, how accurately can we estimate x sub 0 and z sub 0? As part of the investigation, we also find optimal rates of convergence for estimating f, g and their derivatives.
Item Description:	Offprint: Annals of statistics. Funding information taken from leaf 26.
Physical Description:	28 leaves ; 28 cm
Bibliography:	Includes bibliographical references (leaves 27-28).