Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression

@article{Takeuchi2009NonparametricCD,
  title={Nonparametric Conditional Density Estimation Using Piecewise-Linear Solution Path of Kernel Quantile Regression},
  author={Ichiro Takeuchi and Kaname Nomura and Takafumi Kanamori},
  journal={Neural Computation},
  year={2009},
  volume={21},
  pages={533-559}
}
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… CONTINUE READING
Highly Cited
This paper has 126 citations. REVIEW CITATIONS

Citations

Publications citing this paper.

126 Citations

01020'09'12'15'18
Citations per Year
Semantic Scholar estimates that this publication has 126 citations based on the available data.

See our FAQ for additional information.

Similar Papers

Loading similar papers…