Conditional logspline density estimation
Conditional logspline density estimation
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条件对数样条密度估计
DOI：
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发表时间：
1999
期刊：
影响因子：
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通讯作者：
Y. Truong
中科院分区：
文献类型：
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作者：
Benoît R. Mâacsse;Y. Truong
在条件对数样条建模中，条件密度函数的对数logf （y|x）通过使用多项式样条及其张量积来建模。模型的参数（样条函数的系数）通过最大化条件对数似然函数来估计。结果估计是一个密度函数（正的，积分为1），并且是两次连续可微的。该估计进一步用于以自然的方式获得回归和分位数函数的估计。提出了一种基于AIC最小化的自动选择结数和结位的程序。给出了一个实际数据的算例。最后，讨论了条件对数样条模型的扩展和进一步应用。
In conditional logspline modelling, the logarithm of the conditional density function, log f(y|x), is modelled by using polynomial splines and their tensor products. The parameters of the model (coefficients of the spline functions) are estimated by maximizing the conditional log‐likelihood function. The resulting estimate is a density function (positive and integrating to one) and is twice continuously differentiable. The estimate is used further to obtain estimates of regression and quantile functions in a natural way. An automatic procedure for selecting the number of knots and knot locations based on minimizing a variant of the AIC is developed. An example with real data is given. Finally, extensions and further applications of conditional logspline models are discussed.