Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression
Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression
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DOI：
10.1162/neco.2008.10-07-628
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发表时间：
2009-02
影响因子：
2.9
通讯作者：
I. Takeuchi;K. Nomura;T. Kanamori
中科院分区：
文献类型：
--
作者：
I. Takeuchi;K. Nomura;T. Kanamori
回归分析的目的是描述输入向量x和标量输出y之间的随机关系，这可以通过估计整个条件密度p(Yx)来实现。本文提出了一种新的非参数条件密度估计方法。提出了一种基于核的分位数回归的分段线性路径跟踪方法。它使我们能够以分段线性的形式估计p(Yx)在输入域中所有x的累积分布函数。理论分析和实验结果表明了该方法的有效性。
The goal of regression analysis is to describe the stochastic relationship between an input vector x and a scalar output y. This can be achieved by estimating the entire conditional density p(y x). In this letter, we present a new approach for nonparametric conditional density estimation. We develop a piecewise-linear path-following method for kernel-based quantile regression. It enables us to estimate the cumulative distribution function of p(y x) in piecewise-linear form for all x in the input domain. Theoretical analyses and experimental results are presented to show the effectiveness of the approach.