SELF-MODELING WITH RANDOM SHIFT AND SCALE-PARAMETERS AND A FREE-KNOT SPLINE SHAPE FUNCTION
SELF-MODELING WITH RANDOM SHIFT AND SCALE-PARAMETERS AND A FREE-KNOT SPLINE SHAPE FUNCTION
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DOI：
10.1002/sim.4780141807
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发表时间：
1995-09-30
影响因子：
2
通讯作者：
LINDSTROM, MJ
中科院分区：
文献类型：
--
作者：
LINDSTROM, MJ
形状不变模型是一种半参数方法，用于根据聚类数据（对多个个体中的每个个体进行多次观察）估计函数关系。通过在汇集形状信息的同时调整个体尺度差异来估计个体的共同响应曲线形状。在实践中，公共响应曲线仅限于一些灵活的函数族。本文介绍了自由结样条形状函数的使用，并通过假设控制形状函数的单独缩放的参数的随机分布来减少形状不变模型中的参数数量。提出了新的图形诊断，讨论了参数可识别性和估计，并给出了一个例子。
The shape invariant model is a semi-parametric approach to estimating a functional relationship from clustered data (multiple observations on each of a number of individuals). The common response curve shape over individuals is estimated by adjusting for individual scaling differences while pooling shape information. In practice, the common response curve is restricted to some flexible family of functions. This paper introduces the use of a free-knot spline shape function and reduces the number of parameters in the shape invariant model by assuming a random distribution on the parameters that control the individual scaling of the shape function. New graphical diagnostics are presented, parameter identifiability and estimation are discussed, and an example is presented.