Uniqueness, consistency and optimality in spherical regression experiments

For designed experiments based on the spherical regression model of Chang (Ann. Statist. 14 (1986) 907) we provide results on the minimum number of covariate directions that are necessary and sufficient for uniqueness and consistency of least squares estimates and on minimizing confidence regions.

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Bibliographic Details
Published in:	Statistics & probability letters Vol. 54; no. 1; pp. 61 - 65
Main Authors:	Shin, Hwashin H., Takahara, Glen K., Murdoch, Duncan J.
Format:	Journal Article
Language:	English
Published:	Amsterdam Elsevier B.V 01-08-2001 Elsevier
Series:	Statistics & Probability Letters
Subjects:	Consistency Design of experiments Directional data Directional data Spherical regression Design of experiments Uniqueness Consistency Optimality Exact sciences and technology Experimental design Linear inference, regression Mathematics Optimality Probability and statistics Sciences and techniques of general use Spherical regression Statistics Uniqueness Directional data Spherical regression Design of experiments Optimality Uniqueness Consistency Solution uniqueness Non linear regression Statistical estimation Statistical method Statistical regression Numerical data Experimental design Optimality criterion Linear estimation Optimal design(statistics) Numerical example
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Description
Summary:	For designed experiments based on the spherical regression model of Chang (Ann. Statist. 14 (1986) 907) we provide results on the minimum number of covariate directions that are necessary and sufficient for uniqueness and consistency of least squares estimates and on minimizing confidence regions.
ISSN:	0167-7152 1879-2103
DOI:	10.1016/S0167-7152(01)00053-0