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{Summary: We are interested in the comparison, under a third-order framework, of classes of second-order, reduced-bias tail index estimators, giving particular emphasis to minimum-variance reduced-bias estimators of the tail index $\gamma$. The full asymptotic distributional properties of the proposed classes are derived under a third-order framework and the estimators are compared with other alternatives, not only asymptotically, but also for finite samples through Monte Carlo techniques. An application to the log-exchange rates of the Euro against the USA Dollar is also provided.}

Some asymptotic expansions non necessarily related to the central limit theorem are discussed. After observing that the smoothing inequality of Esseen implies the proximity, in the Kolmogorov distance sense, of the distributions of the random variables of two random sequences satisfying a sort of general asymptotic relation, two instances of this observation are presented. A first example, partially motivated by the the statistical theory of high precision measurements, is given by a uniform asymptotic approximation to $(g(X+ μ_n))_{n ın \mathbbm{N}}$, where $g$ is some smooth function, $X$ is a random variable having a moment and a bounded density and $(μ_{n})_{n ın \mathbbm{N}}$ is a sequence going to infinity; the multivariate case as well as the proofs and a complete set of references will be published elsewhere. We next present a second class of examples given by a randomization of the interesting parameter in some classical asymptotic formulas, namely, a generic Laplace's type integral, by the sequence $(μ_n X)_{n ın \mathbbm{N}}$, $X$ being a Gamma distributed random variable. Finally, a simulation study of this last example is presented in order to stress the quality of asymptotic approximations proposed.

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Transactional memory is a new trend in concurrency control that was boosted by the advent of multi-core processors and the near to come many-core processors. It promises the performance of finer grain with the simplicity of coarse grain threading. However, there is a clear absence of software development tools oriented to the transactional memory programming model, which is confirmed by the very small number of related scientific works published until now. This paper describes ongoing work. We propose a very low overhead monitoring framework, developed specifically for monitoring TM computations, that collects the transactional events into a single log file, sorted in a global order. This framework is then used by a visualization tool to display different types of charts from two categories: statistical charts and thread-time space diagrams. These last diagrams are interactive, allowing to identify conflicting transactions. We use the visualization tool to analyse the behavior of two different, but similar, testing applications, illustrating how it can be used to better understand the behavior of these transactional memory applications.

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