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Locally R-optimal designs for a class of nonlinear multiple regression models
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作者 Lei He Rong-Xian Yue 《Statistical Theory and Related Fields》 CSCD 2023年第2期107-120,共14页
This paper concerns with optimal designs for a wide class of nonlinear models with informa-tion driven by the linear predictor.The aim of this study is to generate an R-optimal design which minimizes the product of th... This paper concerns with optimal designs for a wide class of nonlinear models with informa-tion driven by the linear predictor.The aim of this study is to generate an R-optimal design which minimizes the product of the main diagonal entries of the inverse of the Fisher informa tion matrix at certain values of the parameters.An equivalence theorem for the locally R optimal designs is provided in terms of the intensity function.Analytic solutions for the locally saturated R-optimal designs are derived for the models having linear predictors with and without intercept,respectively.The particle swarm optimization method has been employed to generate locally non-saturated R-optimal designs.Numerical examples are presented for ilustration of the locally R-optimal designs for Poisson regression models and proportional hazards regression models. 展开更多
关键词 Poisson regression models proportional hazards models R-optimality particle swarm optimization
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