Contributions in Merlin

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We present a theoretical basis for testing related endpoints. Typically, it is known how to construct tests of the individual hypotheses, and the problem is how to combine them into a multiple test procedure that controls the familywise error rate. Using the closure method, we emphasize the role of consonant procedures, from an interpretive as well as a theoretical viewpoint, and introduce a new procedure, which is consonant and has a maximin property under the normal model. The results are then applied to PROactive, a clinical trial designed to investigate the effectiveness of a glucose-lowering drug on macrovascular outcomes among patients with type 2 diabetes.

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While the positive growth effects of financial integration are extensively documented, little is known of its impact on small and young firms. This paper aims to fill this void relying on a panel of 60,000 firm-year observations on listed and unlisted companies in Eastern European economies to assess the differential impact of foreign bank lending on firm growth and financing. Foreign lending stimulates growth in firm sales, assets, and use of financial debt even though the effect is dampened for small firms. More strikingly, young firms benefit most from foreign bank presence, while businesses connected to domestic banks or to the government suffer. Overall, our findings suggest that foreign banks can help to mitigate connected-lending problems and to improve capital allocation.

We investigate how bank organization shapes banking competition. We show that a bank's geographical lending reach and loan pricing strategy is determined by its own and its rivals’ organizational structure. We estimate the impact of organization on the geographical reach and loan pricing of a large bank. We find that the reach of the bank is smaller when rival banks are large and hierarchically organized, have superior communication technology, have a narrower span of organization, and are closer to a decision unit with lending authority. Rival banks’ size and the number of layers to a decision unit soften spatial pricing.

Companies are increasingly discovering the potential of collaborating with others to create innovative products - often across industries - by combining their specializations in a unique way. A pre-condition for the success of these ventures is to build a common knowledge base, usually by duplicating selected parts of the partner’s knowledge. In this article, a case study and a quantitative study seek to elaborate on the unanswered question: Which building blocks constitute a common knowledge base?

This paper investigates a nonparametric modular neural network (MNN) model to price the S&P-500 European call options. The modules are based on time to maturity and moneyness of the options. The option price function of interest is homogeneous of degree one with respect to the underlying index price and the strike price. When compared to an array of parametric and nonparametric models, the MNN method consistently exerts superior out-of-sample pricing performance. We conclude that modularity improves the generalization properties of standard feedforward neural network option pricing models (with and without the homogeneity hint).

Background: Early risk stratification is important in the management of patients with acute coronary syndromes (ACS). Objective: To develop a rapidly available risk stratification tool for use in all ACS. Design and methods: Application of modern data mining and machine learning algorithms to a derivation cohort of 7520 ACS patients included in the AMIS (Acute Myocardial Infarction in Switzerland)-Plus registry between 2001 and 2005; prospective model testing in two validation cohorts. Results: The most accurate prediction of in-hospital mortality was achieved with the “Averaged One-Dependence Estimators” (AODE) algorithm, with input of 7 variables available at first patient contact: Age, Killip class, systolic blood pressure, heart rate, pre-hospital cardio-pulmonary resuscitation, history of heart failure, history of cerebrovascular disease. The c-statistic for the derivation cohort (0.875) was essentially maintained in important subgroups, and calibration over five risk categories, ranging from <1% to >30% predicted mortality, was accurate. Results were validated prospectively against an independent AMIS-Plus cohort (n=2854, c-statistic 0.868) and the Krakow-Region ACS Registry (n=2635, c-statistic 0.842). The AMIS model significantly outperformed established “point-of-care” risk prediction tools in both validation cohorts. In comparison to a logistic regression-based model, the AODE-based model proved to be more robust when tested on the Krakow validation cohort (c-statistic 0.842 vs. 0.746). Accuracy of the AMIS model prediction was maintained at 12-months follow-up in an independent cohort (n=1972, c-statistic 0.877). Conclusions: The AMIS model is a reproducibly accurate point-of-care risk stratification tool for the complete range of ACS, based on variables available at first patient contact.