摘要
提出了广义变系数模型函数系数的一种新的估计方法.
我们用B样条函数逼近函数系数,不具体选择节点
的个数,而是节点个数取均匀的无信息先验,样条函数系数取正态先验,
用Bayesian模型平均的方法估计各个函数系数.
这种估计方法一个主要特点是允许各个函数系数所需节点个数的后验分布不同, 因此允许
不同函数系数使用不同的光滑参数. 另外,本文还给出了Bayesian B
样条估计的计算方法,并通过模拟例子,
说明广义变系数模型的函数系数可以由Bayesian B
样条估计方法得到很好的估计.
Abstract
This article presents a new approach
of estimating generalized varying-coef-ficient models.
The functional coefficients are approximated by B-spline functions.
We do not select the number of the knots, but use the uniform noninformative prior estimation instead.
The prior estimation of the coefficients of the B-spline functions is taken
as normal distribution. The functional coefficients are estimated by the methods of the
Bayesian model averaging. The advantage of this methods is that the smoothing parameter of each
functional coefficient is admitted to be different because of the different
posterior estimations of the knot number. In addition, the algorithm of Bayesian B-spline estimation
is also given. The simulated examples
show that the functional coefficients of the generalized varying-coefficient model are well estimated by Bayesian B-spline methods.
关键词
广义变系数模型 /
Bayesian模型平均 /
B样条
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Key words
Generalized varying-coefficient models /
Bayesian model averaging /
B-spline function /
Laplace's appro
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卢一强
, 茆诗松.
, {{custom_author.name_cn}}.
广义变系数模型的Bayesian B样条估计. 系统科学与数学, 2006, 26(2): 169-177. https://doi.org/10.12341/jssms08699
Lu Yiqiang
, Mao Shisong.
, {{custom_author.name_en}}.
Bayesian B-Spline Estmation Of The Generalized Varying-Coefficient Models. Journal of Systems Science and Mathematical Sciences, 2006, 26(2): 169-177 https://doi.org/10.12341/jssms08699
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脚注
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