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Locally D-optimal designs for multistage models and heteroscedastic polynomial regression models

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Publication:2500656
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DOI10.1016/j.jspi.2004.11.015zbMath1104.62085OpenAlexW1988838831MaRDI QIDQ2500656

Douglas P. Wiens, Zhide Fang, Zheyang Wu

Publication date: 17 August 2006

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2004.11.015


zbMATH Keywords

D-optimalityoptimal designdose responsebioassay


Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Optimal statistical designs (62K05) General nonlinear regression (62J02)




Cites Work

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  • General equivalence theory for optimum designs (approximate theory)
  • Bayesian Minimally SupportedD-Optimal Designs for an Exponential Regression Model
  • Low-Dose-Rate Extrapolation Using the Multistage Model
  • Recent Advances in Nonlinear Experimental Design
  • Bayesian D-optimal designs on a fixed number of design points for heteroscedastic polynomial models
  • Environmental risk assessment model for low dose extrapolation∗
  • Optimal designs for the logit and probit models for binary data
  • Bayesian and maximin optimal designs for heteroscedastic regression models
  • Locally Optimal Designs for Estimating Parameters
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