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- W4295808637 abstract "The optimization of likelihood based inference through efficient experimental design is considered. A traditional framework for efficiency optimization is to maximize a concave function of expected Fisher information, commonly referred to as optimal design. There exists a substantial amount of literature that suggest that the observed Fisher information is a more relevant measure of the precision of the maximum likelihood estimate. Despite evidence in its favor limited effort has been made to find designs that are optimal with respect to this measure. In this work an adaptive design that incorporates observed Fisher information is proposed. The proposed design is shown to be more efficient than the optimal design with respect to inference. This theoretical result is used to develop a sample size calculation. It is found that fewer samples are required for the proposed design to obtain an equivalent level of precision as the optimal design." @default.
- W4295808637 created "2022-09-15" @default.
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- W4295808637 date "2023-03-01" @default.
- W4295808637 modified "2023-09-27" @default.
- W4295808637 title "Efficiency of observed information adaptive designs in linear models" @default.
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- W4295808637 doi "https://doi.org/10.1016/j.jspi.2022.09.001" @default.
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