Hypergraph Modelling for Geometric Model Fitting

In this paper, we propose a novel hypergraph based method (called HF) to fit and segment multi-structural data. The proposed HF formulates the geometric model fitting problem as a hypergraph partition problem based on a novel hypergraph model. In the hypergraph model, vertices represent data points and hyperedges denote model hypotheses. The hypergraph, with large and "data-determined" degrees of hyperedges, can express the complex relationships between model hypotheses and data points. In addition, we develop a robust hypergraph partition algorithm to detect sub-hypergraphs for model fitting. HF can effectively and efficiently estimate the number of, and the parameters of, model instances in multi-structural data heavily corrupted with outliers simultaneously. Experimental results show the advantages of the proposed method over previous methods on both synthetic data and real images.Comment: Pattern Recognition, 201

Genome-Wide Significance Levels and Weighted Hypothesis Testing

Genetic investigations often involve the testing of vast numbers of related hypotheses simultaneously. To control the overall error rate, a substantial penalty is required, making it difficult to detect signals of moderate strength. To improve the power in this setting, a number of authors have considered using weighted $p$-values, with the motivation often based upon the scientific plausibility of the hypotheses. We review this literature, derive optimal weights and show that the power is remarkably robust to misspecification of these weights. We consider two methods for choosing weights in practice. The first, external weighting, is based on prior information. The second, estimated weighting, uses the data to choose weights.Comment: Published in at http://dx.doi.org/10.1214/09-STS289 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Genome-Wide Association and Linkage Analysis of Quantitative Traits: Comparison pf Likelihood-Ratio Test and Conditional Score Statistic

Over the past decade, genetic analysis has shifted from linkage studies, which identify broad regions containing putative trait loci, to genome-wide association studies, which detect the association of a marker with a specific phenotype. Because linkage and association analysis provide complementary information, developing a method to combine these analyses may increase the power to detect a true association. In this paper we compare a linkage score and association score test as well as a newly proposed combination of these two scores with traditional linkage and association methods.National Institutes of Health (National Institute of General Medical Sciences R01 GM031575, National Center for Research Resources Shared Instrumentation grant 1S10RR163736-01A1

Macroprudential policy and bank systemic risk

This paper investigates the effectiveness of macroprudential policy to contain the systemicrisk of European banks between 2000 and 2017. We use a new database (MaPPED) collected by experts at the ECB and national central banks with narrative informationon a broad range of instruments which are tracked over their life cycle. Using a dynamicpanel framework at a monthly frequency we assess the impact of macroprudential tools and their design on the banks’ systemic risk both in the short and the long run. We furthermore decompose the systemic risk measure in an individual bank risk component and a systemic linkage component. This is of particular interest because microprudential policy focuses on the tail risk of an individual bank while macroprudential policy targets systemic risk by addressing the interlinkages and common exposures across banks. In general, the announcements of macroprudential policy actions have a downward effect on bank systemic risk. On average, all banks benefit from macroprudential tools in terms oftheir individual risk. We find that credit growth tools and exposure limits exhibit the most pronounced downward effect on the individual risk component. However, we find evidence for a risk-shifting effect which is more pronounced for retail-oriented banks. The effects are heterogeneous across banks with respect to the systemic linkage component. Liquidity tools and measures aimed at increasing the resilience of banks decrease the systemic linkage of banks. Moreover, these tools appear to be most effective for distressed banks.Our results have implications for the optimal design of macroprudential instruments