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.

Seasonal Stability Tests in gretl. An Application to International Tourism Data

The seasonal stability tests of Canova & Hansen (1995) (CH) provide a method complementary to that of Hylleberg et al. (1990) for testing for seasonal unit roots. But the distribution of the CH tests are unknown in small samples. We present a method to numerically compute critical values and P-values for the CH tests for any sample size and any seasonal periodicity. In fact this method is applicable to the types of seasonality which are commonly in use, but also to any other.

Influence of chilled and frozen storage on the stability of trout and herring fillet

Effects of chilled and frozen storage on specific enthalpy (ΔH) and transition temperature (Td) of protein denaturation as well as on selected functional properties of muscle tissue of rainbow trout and herring were investigated. The Td of myosin shifted from 39 to 33 °C during chilling of trout post mortem, but was also influenced by pH. Toughening during frozen storage of trout fillet was characterized by an increased storage modulus of a gel made from the raw fillet. Differences between long term and short term frozen stored, cooked trout fillet were identified by a compression test and a consumer panel. These changes did not affect the Td and ΔH of heat denaturation during one year of frozen storage at –20 °C. In contrast the Td of two myosin peaks of herring shifted during frozen storage at –20 °C to a significant lower value and overlaid finally. Myosin was aggregated by hydrophobic protein-protein interactions. Both thermal properties of myosin and chemical composition were sample specific for wild herring, but were relative constant for farmed trout samples over one year. Determination of Td was very precise (standard deviation <2 %) at a low scanning rate (≤ 0.25 K·min-1) and is useful for monitoring the quality of chilled and frozen stored trout and herring.

Biologically-inspired data decorrelation for hyper-spectral imaging

Hyper-spectral data allows the construction of more robust statistical models to sample the material properties than the standard tri-chromatic color representation. However, because of the large dimensionality and complexity of the hyper-spectral data, the extraction of robust features (image descriptors) is not a trivial issue. Thus, to facilitate efficient feature extraction, decorrelation techniques are commonly applied to reduce the dimensionality of the hyper-spectral data with the aim of generating compact and highly discriminative image descriptors. Current methodologies for data decorrelation such as principal component analysis (PCA), linear discriminant analysis (LDA), wavelet decomposition (WD), or band selection methods require complex and subjective training procedures and in addition the compressed spectral information is not directly related to the physical (spectral) characteristics associated with the analyzed materials. The major objective of this article is to introduce and evaluate a new data decorrelation methodology using an approach that closely emulates the human vision. The proposed data decorrelation scheme has been employed to optimally minimize the amount of redundant information contained in the highly correlated hyper-spectral bands and has been comprehensively evaluated in the context of non-ferrous material classification