Decision theory is, in particular in economics, medical expert systems and statistics, an important tool for determining optimal decisions under uncertainty. In view of applications in statistics, the present book is concerned with decision problems which are explicitly data-based. Since the arising uncertainties are often too complex to be described by classical precise probability assessments, concepts of imprecise probabilities (coherent lower previsions, F-probabilities) are applied. Due to the present state of research, some basic groundwork has to be done: Firstly, topological properties of different concepts of imprecise probabilities are investigated. In particular, the concept of coherent lower previsions appears to have advantageous properties for applications in decision theory. Secondly, several decision theoretic tools are developed for imprecise probabilities. These tools are mainly based on concepts developed by L. Le Cam and enable, for example, a definition of sufficiency in case of imprecise probabilities for the first time. Building on that, the article [A. Buja, Zeitschrift für Wahrscheinlichkeitstheorie und Verwandte Gebiete 65 (1984) 367-384] is reinvestigated in the only recently available framework of imprecise probabilities. This leads to a generalization of results within the Huber-

Keywords: In this paper we use register-based genetic programming with memory-withmemory to discover probabilistic membership functions that are used to divide up data-sets of event-related potentials recorded via EEG in psycho-physiological experiments based on the corresponding response times. The objective is to evolve membership functions which lead to maximising the statistical significance with which true brain waves can be reconstructed when averaging the trials in each bin. Results show that GP can significantly improve the fidelity with which ERP components can be recovered. Event-related potentials, Averaging, Register-based GP, Memory-with-Memory 1.

Abstract—Applications are given of a formula for the exact probability density function of the maximum likelihood estimates of a statistical model, where the data generating model is allowed to differ from the estimation model. The main examples are supported by simulation experiments. Curved exponential families are investigated, for which an approach is described that can be used in many practical situations. The distribution of a maximum likelihood estimator in exponential regression is developed. Nonlinear regression is then considered, with an example of a model discrepancy situation arising in ELISA immunoassays and similar biochemical titrations. An incorrect logistic model is specified for a titration curve that is used for describing the reaction of a chemical sample to applied substrate concentration. A method is suggested to reduce the amount of bias in the estimate of binding affinity. Finally there is a prospective discussion of other possible uses of the technique, including general comparisons of sets of alternative models in frequentist and Bayesian settings, applications to robust estimation and extensions beyond maximum likelihood estimates.

The ability of humans for color constancy, i.e. the ability to correct for color deviation caused by a different illumination, is far beyond computer vision performances: nowadays, automatic color constancy is still a difficult problem. This article proposes a new step forward towards solving this color constancy problem. Basically, it consists in learning how illumination can affect some reference objects. During a learning stage, images are taken under various illuminations, allowing for automatic building of a model explaining color changes. The model can explain complex non-linear color transformations with only a few parameters. Therefore, the observation of color variations in a few reference regions (e.g. known object) is enough to estimate the global color changes.

The development process for the implementation of a fast graph layout engine is examined. This implementation is of an algorithm proposed by Davidson, Wylie and Boyack in their paper "Cluster Stability and the Use of Noise in Interpretation of Clustering" . A critical analysis is given of the implementation produced, along with the development process adopted. The implementation has provided a valuable insight into the effectiveness of the said algorithm in practice. It has been discovered from the implementation that the algorithm performs less well than hoped, a fact that has been noted and further investigated.

Any opinions expressed here are those of the author(s) and not those of the IIIS. All works posted here are owned and copyrighted by the author(s). Papers may only be downloaded for personal use only. Spatial linkages in international financial markets Viviana Fernandez 1 Spatial dependency has been broadly studied in several research areas, such as environmental criminology, economic geography, environmental sciences, and urban economics. However, it has been essentially overlooked in other subfields of economics and in the field of finance as a whole. A key element at stake is the definition of contiguity. In the context of financial markets, defining a metric distance is not a simple matter. In this article, we explore the notion of spatial dependency in a panel of 126 Latin American firms from Brazil, Chile, and Mexico over the period 1997-2006. Firstly, we formulate a spatial version of the capital asset pricing model (S-CAPM), which accounts for alternative measures of distance between firms, such as market capitalization, the market-to-book, enterprise value-to-EBITDA, and the debt ratios. Secondly, we analyze the potential existence of spatial linkages in investment and dividend decisions. We conclude that there may be contemporaneous linkages in firms ’ decisions of such ratios, which may be indicative of some strategic behavior. Keywords: spatial panel data, S-CAPM, Tobin’s Q 1

An integrative approach to characterize disease-specific pathways

A robust measure of correlation between two genes on a microarray

In a trio of experiments, a matching procedure generated direct, analogue measures of short-term memory for the spatial frequency of Gabor stimuli. Experiment 1 showed that when just a single Gabor was presented for study, a retention interval of just a few seconds was enough to increase the variability of matches, suggesting that noise in memory substantially exceeds that in vision. Experiment 2 revealed that when a pair of Gabors was presented on each trial, the remembered appearance of one of the Gabors was influenced by: (i) the relationship between its spatial frequency and the spatial frequency of the accompanying, task-irrelevant non-target stimulus; and (ii) the average spatial frequency of Gabors seen on previous trials. These two influences, which work on very different time scales, were approximately additive in their effects, each operating as an attractor for remembered appearance. Experiment 3 showed that a timely pre-stimulus cue allowed selective attention to curtail the influence of a task-irrelevant non-target, without diminishing the impact of stimuli seen on previous trials. It appears that these two separable attractors influence distinct processes, with perception being influenced by the non-target stimulus, and memory being influenced by stimuli seen on previous trials. Keywords: averaging Visual short-term memory, selective-attention, Prototype effect, perceptual Research on visual working memory has been dominated by a focus on questions of quantity, such as the number of stimuli that can be remembered, or for how long. Although this focus has produced valuable insights (Vogel,

A robustness study to investigate the performance of six procedures for comparing two or more groups, under several circumstances, has been carried out. The study was divided in four parts. The first three were Monte-Carlo studies, and the fourth was a study on empirical data. The six procedures were the ANOVA (or its special case, the Student's t) procedure (1), the Satterthwaite (or its special case, the Welch) procedure (2), the bootstrap Satterthwaite (the Welch) procedure (3), the bootstrap percentile procedure for the mean (4) and for the median (5), and the Bonett and Price median procedure (6). The procedures were compared on confidence interval widths and coverage percentages, based on 10.000 replications. In Studies 1 and 2, samples were drawn from simulated population distributions with the same (non-) normality. The conditions were varied with different non-normal distributions, unequal population variances, and different equal or unequal sample sizes. In Study 1 two samples were compared on the mean or median and in Study 2 four samples were compared on the mean or median with a contrast analysis. For Study 3 simulated cardiovascular data obtained by Van Roon's baroreflex model (1998) were used. In this study, the results of logarithmically transformed data were