Geometric and long run aspects of Granger causality

This paper extends multivariate Granger causality to take into account the subspacesalong which Granger causality occurs as well as long run Granger causality. The propertiesof these new notions of Granger causality, along with the requisite restrictions, are derivedand extensively studied for a wide variety of time series processes including linear invertibleprocess and VARMA. Using the proposed extensions, the paper demonstrates that: (i) meanreversion in L2 is an instance of long run Granger non-causality, (ii) cointegration is a specialcase of long run Granger non-causality along a subspace, (iii) controllability is a special caseof Granger causality, and finally (iv) linear rational expectations entail (possibly testable)Granger causality restriction along subspaces.

Parallel scheduling of multiclass M/M/m queues: Approximate and heavy-traffic optimization of achievable performance

We address the problem of scheduling a multiclass $M/M/m$ queue with Bernoulli feedback on $m$ parallel servers to minimize time-average linear holding costs. We analyze the performance of a heuristic priority-index rule, which extends Klimov's optimal solution to the single-server case: servers select preemptively customers with larger Klimov indices. We present closed-form suboptimality bounds (approximate optimality) for Klimov's rule, which imply that its suboptimality gap is uniformly bounded above with respect to (i) external arrival rates, as long as they stay within system capacity;and (ii) the number of servers. It follows that its relativesuboptimality gap vanishes in a heavy-traffic limit, as external arrival rates approach system capacity (heavy-traffic optimality). We obtain simpler expressions for the special no-feedback case, where the heuristic reduces to the classical $c \mu$ rule. Our analysis is based on comparing the expected cost of Klimov's ruleto the value of a strong linear programming (LP) relaxation of the system's region of achievable performance of mean queue lengths. In order to obtain this relaxation, we derive and exploit a new set ofwork decomposition laws for the parallel-server system. We further report on the results of a computational study on the quality of the $c \mu$ rule for parallel scheduling.

Additional utility of insiders with imperfect dynamical information

In this paper we consider an insider with privileged information thatis affected by an independent noise vanishing as the revelation timeapproaches. At this time, information is available to every trader. Ourfinancial markets are based on Wiener space. In probabilistic terms weobtain an infinite dimensional extension of Jacod s theorem to covercases of progressive enlargement of filtrations. The application ofthis result gives the semimartingale decomposition of the originalWiener process under the progressively enlarged filtration. As anapplication we prove that if the rate at which the additional noise inthe insider s information vanishes is slow enough then there is noarbitrage and the additional utility of the insider is finite.

Power transformations in correspondence analysis

Power transformations of positive data tables, prior to applying the correspondence analysis algorithm, are shown to open up a family of methods with direct connections to the analysis of log-ratios. Two variations of this idea are illustrated. The first approach is simply to power the original data and perform a correspondence analysis this method is shown to converge to unweighted log-ratio analysis as the power parameter tends to zero. The second approach is to apply the power transformation to thecontingency ratios, that is the values in the table relative to expected values based on the marginals this method converges to weighted log-ratio analysis, or the spectral map. Two applications are described: first, a matrix of population genetic data which is inherently two-dimensional, and second, a larger cross-tabulation with higher dimensionality, from a linguistic analysis of several books.

Use of forensic investigations in anti-doping

The fight against doping is mainly focused on direct detection, using analytical methods for the detection of doping agents in biological samples. However, the World Anti-Doping Code also defines doping as possession, administration or attempted administration of prohibited substances or methods, trafficking or attempted trafficking in any prohibited substance or methods. As these issues correspond to criminal investigation, a forensic approach can help assessing potential violation of these rules.In the context of a rowing competition, genetic analyses were conducted on biological samples collected in infusion apparatus, bags and tubing in order to obtain DNA profiles. As no database of athletes' DNA profiles was available, the use of information from the location detection as well as contextual information were key to determine a population of suspected athletes and to obtain reference DNA profiles for comparison.Analysis of samples from infusion systems provided 8 different DNA profiles. The comparison between these profiles and 8 reference profiles from suspected athletes could not be distinguished.This case-study is one of the first where a forensic approach was applied for anti-doping purposes. Based on this investigation, the International Rowing Federation authorities decided to ban not only the incriminated athletes, but also the coaches and officials for 2 years.