NUMERICAL PARAMETER IDENTIFIABILITY AND ESTIMABILITY - INTEGRATING IDENTIFIABILITY, ESTIMABILITY, AND OPTIMAL SAMPLING DESIGN
NUMERICAL PARAMETER IDENTIFIABILITY AND ESTIMABILITY - INTEGRATING IDENTIFIABILITY, ESTIMABILITY, AND OPTIMAL SAMPLING DESIGN
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DOI：
10.1016/0025-5564(85)90098-7
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发表时间：
1985-12-01
影响因子：
4.3
通讯作者：
GREIF, P
中科院分区：
文献类型：
--
作者：
JACQUEZ, JA;GREIF, P
我们定义了两个级别的参数。基本参数与模型和实验(S)相关。然而，观测定义了一组可识别的观测参数，这些参数是基本参数的函数。从这个公式出发，我们证明了隐函数方法为检验局部可辨识性和可估计性提供了一个共同的基础，并引入了最优抽样设计问题。基于在初始参数估计时生成的大量但有限的观测值的最小二乘方法，然后给出局部可辨识性、可估计性和生成最优采样计划的统一方法。
We define two levels of parameters. The basic parameters are associated with the model and experiment(s). However, the observations define a set of identifiable observational parameters that are functions of the basic parameters. Starting with this formulation, we show that an implicit function approach provides a common basis for examining local identifiability and estimability and gives a lead-in to the problem of optimal sampling design. A least squares approach based on a large but finite set of observations generated at initial parameter estimates then gives a uniform approach to local identifiability, estimability, and the generation of an optimal sampling schedule.