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Consistency of the maximum likelihood estimator and Bayesian estimator based on sequential sensitivity experiments

机译:基于连续敏感性实验的最大似然估计和贝叶斯估计的一致性

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摘要

It is often of interest to estimate parameters of a response curve based on sequential sensitivity experiments. However, there is no general method to establish consistency of the maximum likelihood estimators or Bayesian estimators. In this paper, we focus on a general sequential design with a general parametric model. Under mild conditions, we prove the consistency of the estimators. Also we give a higher order approximation for the posterior expectation.
机译:基于顺序灵敏度实验估计响应曲线的参数通常是令人感兴趣的。但是,没有通用的方法来确定最大似然估计或贝叶斯估计的一致性。在本文中,我们将重点放在具有通用参数模型的通用顺序设计上。在温和的条件下,我们证明了估计量的一致性。同样,我们给出后验期望的高阶近似。

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