This paper classifies statistical methodologies available for the marketrisk measurement. With the help of the weighted likelihood, a broad class ofnon-normal distributions, which are not generally considered so far, areapplied to possibly hetero-scedastic financial variables. The approach is compared with popular procedures such as GARCH and J. P. Morgan's using daily dataof 12 financial variables.

Motivated by a real-world data set of more than 40 000 observations, we consider single linkage clustering of large data sets in high-dimensional spaces. In this particular case, the data we consider are observations in the space of one-dimensional submanifolds of Euclidean space. This data set is so large that the individual observations cannot each be inspected. A goal of the analysis is to produce a partial ordering of the data so that a useful inspection of selected observations can take place. An essential step is construction of a metric on the objects in this high-dimensional space that can be computed fairly quickly. The size of the problem makes the running time of Prim's algorithm (the fastest available algorithm for finding a minimal spanning tree of a complete graph) prohibitive. We have developed several methods which approximate the single linkage tree. We report the result of applying our approximations to the data set.

We describe methods developed to interpolate and project flight paths of aircraft in controlled airspace over the continental United States from aperiodic position reports. There are a number of unusual features of the dynamic displays we have developed. Our visualizations can be viewed from either a fixed or moving viewpoint. The direction and distance of the focal point from the viewing point is under program control (allowing viewing in a direction other than the direction of motion of the viewpoint). The maximum and minimum depth of field is under program control (allowing viewing of selected local subsets of the data).