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  • Title: Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression.
    Author: Takeuchi I, Nomura K, Kanamori T.
    Journal: Neural Comput; 2009 Feb; 21(2):533-59. PubMed ID: 19196229.
    Abstract:
    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.
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