A kernel-based parametric method for conditional density estimation

作者：Fu Gang; Shih Frank Y^*; Wang Haimin

来源：Pattern Recognition, 2011, 44(2): 284-294.

DOI：10.1016/j.patcog.2010.08.027

• Conditional density estimation
• Kernel principal component analysis
• Kernel function
• Nadaraya-Watson estimator

A conditional density function, which describes the relationship between response and explanatory variables, plays an important role in many analysis problems. In this paper, we propose a new kernel-based parametric method to estimate conditional density. An exponential function is employed to approximate the unknown density, and its parameters are computed from the given explanatory variable via a nonlinear mapping using kernel principal component analysis (KPCA). We develop a new kernel function, which is a variant to polynomial kernels, to be used in KPCA. The proposed method is compared with the Nadaraya-Watson estimator through numerical simulation and practical data. Experimental results show that the proposed method outperforms the Nadaraya-Watson estimator in terms of revised mean integrated squared error (RMISE). Therefore, the proposed method is an effective method for estimating the conditional densities.

• 出版日期2011-2