An efficient design for model discrimination and parameter estimation in linear models
兼顾线性模型辨别与参数估计的有效试验设计
We consider experimental designs in a regression set‐up where the unknown regression function belongs to a known family of nested linear models. The objective of our design is to select the correct model from the family of nested models as well as to estimate efficiently the parameters associated with that model. We show that our proposed design is able to choose the true model with probability tending to one as the number of trials grows to infinity. We also establish that…