Locally D-optimal designs for exponential regression models

Holger Dette; Viatcheslav B. Melas; Weng Kee Wong

Locally D-optimal designs for exponential regression models

Holger Dette
, Viatcheslav B. Melas
, Weng Kee Wong

Department of Statistics

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

30 Citations (Scopus)

Abstract

We study locally D-optimal designs for some exponential models that are frequently used in the biological sciences. The model can be written as an algebraic sum of two or three exponential terms. We show that approximate locally D-optimal designs are supported at a minimal number of points and construct these designs numerically.

Original language	English
Pages (from-to)	789-803
Number of pages	15
Journal	Statistica Sinica
Volume	16
Issue number	3
Publication status	Published - 2006 Jul

All Science Journal Classification (ASJC) codes

Statistics and Probability
Statistics, Probability and Uncertainty

Cite this

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title = "Locally D-optimal designs for exponential regression models",

abstract = "We study locally D-optimal designs for some exponential models that are frequently used in the biological sciences. The model can be written as an algebraic sum of two or three exponential terms. We show that approximate locally D-optimal designs are supported at a minimal number of points and construct these designs numerically.",

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AB - We study locally D-optimal designs for some exponential models that are frequently used in the biological sciences. The model can be written as an algebraic sum of two or three exponential terms. We show that approximate locally D-optimal designs are supported at a minimal number of points and construct these designs numerically.

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Locally D-optimal designs for exponential regression models

Abstract

All Science Journal Classification (ASJC) codes

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