变系数模型的Bayes样条估计

Bayesian Spline Estimation of the Varying-coefficient Models

  • 摘要: 本文考察变系数模型y=x1β1(t)+x2β2(t)+…+xpβp(t)+,~N(0,σ2),βr(t),r=1,…,P是光滑的连续函数.假定βr(t)是三阶的B样条函数,给结点的个数一个均匀先验,用贝叶斯模型平均的方法估计函数系数,这种估计方法充分考虑到各个函数系数的差别,允许不同的函数系数有不同的结点个数,即允许不同的函数系数使用不同的光滑参数.

     

    Abstract: The model being studied in this paper is the varying-coefficient model y=x1β1(t)+x2β2(t)+…+xpβp(t)+, where ~N(0,σ2), and βr(t),r=1,…,P are continuous smooth function. Suppose that the function coefficient βr(t) are the spline functions of degree three and the knot number is given the uniform prior. The coefficient functions are estimated by the method of the Bayesian model averaging. This methods take advantage of the smoothing parameter of each coefficient function being allowed to be different and considering the uncertainty of the knot number.

     

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