Nonparametric conditional density estimation using piecewise-linear solution path of kernel quantile regression.
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.
Takeuchi
Ichiro
I
Department of Scientific and Engineering Simulation, Graduate School of Engineering, Nagoya Institute of Technology, Syowa-ku, Nagoya 466-8555, Japan. takeuchi.ichiro@nitech.ac.jp
Nomura
Kaname
K
Kanamori
Takafumi
T
D000367:Age Factors; D000465:Algorithms; D015519:Bone Density; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D005260:Female; D006798:Housing / Q000191:economics / Q000706:statistics & numerical data; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D012044:Regression Analysis; D018709:Statistics, Nonparametric