Forward-looking Pairwise Stability in Networks with Externalities

We consider cooperation situations where players have network relations. Networks evolve according to a stationary transition probability matrix and at each moment in time players receive payoffs from a stationary allocation rule. Players discount the future by a common factor. The pair formed by an allocation rule and a transition probability matrix is called a forward-looking network formation scheme if, first, the probability that a link is created is positive if the discounted, expected gains to its two participants are positive, and if, second, the probability that a link is eliminated is positive if the discounted, expected gains to at least one of its two participants are positive. The main result is the existence, for all discount factors and all value functions, of a forward-looking network formation scheme. Furthermore, we can always nd a forward-looking network formation scheme such that (i) the allocation rule is component balanced and (ii) the transition probabilities increase in the di erence in payo s for the corresponding players responsible for the transition. We use this dynamic solution concept to explore the tension between e ciency and stability.

A Note on Wavelet Correlation and Cointegration

In a recent paper Leong-Huang:2010 {Journal of Applied Statistics 37, 215–233} proposed a wavelet-correlation-based approach to test for cointegration between two time series. However, correlation and cointegration are two different concepts even when wavelet analysis is used. It is known that statistics based on nonstationary integrated variables have non-standard asymptotic distributions. However, wavelet analysis offsets the integrating order of nonstationary series so that traditional asymptotics on stationary variables suffices to ascertain the statistical properties of wavelet-based statistics. Based on this, this note shows that wavelet correlations cannot be used as a test of cointegration.

Contributions on distance-based algorithms, multi-classifier construction and pairwise classification

179 p.

Fluorescence cross-correlation spectroscopy to study hemodynamics

[EN] In the recent years a series of optical correlation techniques have been developed in order to be able to measure flow velocity with high spatial resolution while being non-invasive in order to be employed in-vivo on biological organisms. The technique employed in my thesis work, scanning laser image correlation (SLIC), is a powerful approach for the detection of flow motions because it overcomes some limitations of the classical spectroscopy techniques. SLIC method consists in repeated laser scans over a linear pattern and on the cross correlation of the signal emitted by the excited fluorophores in different positions along the scan line. Therefore, the resulting measurements for flow velocity are really accurate.