The Credit Default Swap (CDS) market has both been lauded for its ability to stabilize the financial system through credit risk transfers and been the source of regulatory concern due to its size and lack of transparency. As a decentralized over-the-counter market, detailed information about...

The classical approach to testing for structural change employs retrospective tests using a historical data set of a given length. Here we consider a wide array of fluctuation-type tests in a monitoring situation - given a history period for which a regression relationship is known to be stable,...

When running agent-based simulations using ready-made components, one usually faces heterogenity problems both for the agents implementation and for the underlying platform. To circumvent these kind of hindrances, we introduce a wrapper technique for mapping the functionality of agents living in...

Autoren-Abstract: This paper introduces ideas and methods for testing for structural change in linear regression models and presents how these have been realized in an R package called strucchange. It features tests from the generalized fluctuation test framework as well as from the F test (Chow...

In this paper we propose an unsupervised voting-merging scheme that is capable of clustering data sets, and also of finding the number of clusters existing in them. The voting part of the algorithm allows us to combine several runs of clustering algorithms resulting in a common partition.(...)

n this paper we present an unsupervised algorithm which performs clustering given a data set and which can also find the number of clusters existing in it. This algorithm consists of two techniques...

This paper we deals with a fuzzy voting scheme for cluster algorithms.

The assessment of the performance of learners by means of benchmark experiments is established exercise. In practice, benchmark studies are a tool to compare the performance of several competing algorithms for a certain learning problem. Cross-validation or resampling techniques are commonly...

In this paper we introduce a mixed approach for the semi-supervised data problem. Our approach consists of an ensemble unsupervised learning part where the labeled and unlabeled points are segmented into clusters.(...)

We introduce a generic simulation framework suitable for agent-based simulations featuring the support of heterogeneous agents, hierarchical scheduling and flexible specification of design parameters.(...)