Generalized Prediction Intervals for Arbitrary Distributed High-Dimensional Data
arXiv:0809.3352
Abstract
This paper generalizes the traditional statistical concept of prediction intervals for arbitrary probability density functions in high-dimensional feature spaces by introducing significance level distributions, which provides interval-independent probabilities for continuous random variables. The advantage of the transformation of a probability density function into a significance level distribution is that it enables one-class classification or outlier detection in a direct manner.
13 pages, 3 figures