Convex formulation for nonparametric estimation of mixing distribution


We discuss a nonparametric estimation method of the mixing distribution in mixture models. We propose an objective function with one parameter, where its minimization becomes the maximum likelihood estimation or the kernel vector quantization in special cases. Generalizing Lindsay’s theorem for the nonparametric maximum likelihood estimation, we prove the existence and discreteness of the optimal mixing distribution and devise an algorithm to calculate it. Furthermore, we show the connection between the unifying estimation framework and the rate-distortion problem. It is demonstrated that with an appropriate choice of the parameter, the proposed method is less prone to overfitting than the maximum likelihood method.

Proceedings of The Fifth Workshop on Information Theoretic Methods in Science and Engineering