Multivariate parametric density estimation based on the modified Cramér-von Mises distance | IEEE Conference Publication | IEEE Xplore

Multivariate parametric density estimation based on the modified Cramér-von Mises distance


Abstract:

In this paper, a novel distance-based density estimation method is proposed, which considers the overall density function in the goodness-of-fit. In detail, the parameter...Show More

Abstract:

In this paper, a novel distance-based density estimation method is proposed, which considers the overall density function in the goodness-of-fit. In detail, the parameters of Gaussian mixture densities are estimated from samples, based on the distance of the cumulative distributions over the entire state space. Due to the ambiguous definition of the standard multivariate cumulative distribution, the Localized Cumulative Distribution and a modified Cramér-von Mises distance measure are employed. A further contribution is the derivation of a simple-to-implement optimization procedure for the optimization problem. The proposed approach's good performance in estimating arbitrary Gaussian mixture densities is shown in an experimental comparison to the Expectation Maximization algorithm for Gaussian mixture densities.
Date of Conference: 05-07 September 2010
Date Added to IEEE Xplore: 14 October 2010
ISBN Information:
Conference Location: Salt Lake City, UT, USA

References

References is not available for this document.