Constrained optimal designs for regression models

Carl M.S. Lee

doi:10.1080/03610928708829401

Constrained optimal designs for regression models

Carl M.S. Lee

Research output: Contribution to journal › Article › peer-review

23 Scopus citations

Abstract

An attempt of combining several optimality criteria simultaneously by using the techniques of nonlinear programming is demonstrated. Four constrained D- and G-optimality criteria are introduced, namely, D-restricted, Ds-restricted A-restricted and E-restricted D- and G-optimality. The emphasis is particularly on the polynomial regression. Examples for quadratic polynomial regression are investigated to illustrate the applicability of these constrained optimality criteria.

Original language	English
Pages (from-to)	765-783
Number of pages	19
Journal	Communications in Statistics - Theory and Methods
Volume	16
Issue number	3
DOIs	https://doi.org/10.1080/03610928708829401
State	Published - Jan 1987
Externally published	Yes

Keywords

Constrained optimality
convex analysis
efficiency
optimal design
quadratic regression

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10.1080/03610928708829401

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AB - An attempt of combining several optimality criteria simultaneously by using the techniques of nonlinear programming is demonstrated. Four constrained D- and G-optimality criteria are introduced, namely, D-restricted, Ds-restricted A-restricted and E-restricted D- and G-optimality. The emphasis is particularly on the polynomial regression. Examples for quadratic polynomial regression are investigated to illustrate the applicability of these constrained optimality criteria.

KW - Constrained optimality

KW - convex analysis

KW - efficiency

KW - optimal design

KW - quadratic regression

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DO - 10.1080/03610928708829401

M3 - Article

AN - SCOPUS:0001190893

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SP - 765

EP - 783

JO - Communications in Statistics - Theory and Methods

JF - Communications in Statistics - Theory and Methods

IS - 3

ER -

Constrained optimal designs for regression models

Abstract

Keywords

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