Conditional quantiles with varying Gaussians
Conditional quantiles with varying Gaussians
复制标题
具有不同高斯的条件分位数
DOI：
10.1007/s10444-011-9257-5
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发表时间：
2013-05-01
影响因子：
1.7
通讯作者：
Xiang, Dao-Hong
中科院分区：
文献类型：
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作者：
Xiang, Dao-Hong
在本文中，我们研究条件分位数回归的学习算法产生的Tikhonov正则化计划与弹球损失和变化的高斯内核。我们的主要目标是提供算法的收敛速度，并说明条件分位数回归和最小二乘回归之间的差异。应用变化的高斯核提高了算法的逼近能力。样本误差的界是通过使用投影算子，方差期望界来自条件分布的条件和紧界的覆盖数涉及高斯内核。
In this paper we study conditional quantile regression by learning algorithms generated from Tikhonov regularization schemes associated with pinball loss and varying Gaussian kernels. Our main goal is to provide convergence rates for the algorithm and illustrate differences between the conditional quantile regression and the least square regression. Applying varying Gaussian kernels improves the approximation ability of the algorithm. Bounds for the sample error are achieved by using a projection operator, a variance-expectation bound derived from a condition on conditional distributions and a tight bound for the covering numbers involving the Gaussian kernels.