Spis treści: MS 3(1)

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Kernel Estimation of Rate Function for Recurrent Event Data

Recurrent event data are largely characterized by the rate function but smoothing techniques for estimating the rate function have never been rigorously developed or studied in statistical literature. This paper considers the moment and least squares methods for estimating the rate function from recurrent event data. With an independent censoring assumption on the recurrent event process, we study statistical properties of the proposed estimators and propose bootstrap procedures for the bandwidth selection and for the approximation of confidence intervals in the estimation of the occurrence rate function. It is identified that the moment method without resmoothing via a smaller bandwidth will produce a curve with nicks occurring at the censoring times, whereas there is no such problem with the least squares method. Furthermore, the asymptotic variance of the least squares estimator is shown to be smaller under regularity conditions. However, in the implementation of the bootstrap procedures, the moment method is computationally more efficient than the least squares method because the former approach uses condensed bootstrap data. The performance of the proposed procedures is studied through Monte Carlo simulations and an epidemiological example on intravenous drug users. Copyright 2005 Board of the Foundation of the Scandinavian Journal of Statistics..

How to Certify the Leakage of a Chip?

Evaluating side-channel attacks and countermeasures requires determining the amount of information leaked by a target device. For this purpose, information extraction procedures published so far essentially combine a leakage model" with a distinguisher". Fair evaluations ideally require exploiting a perfect leakage model (i.e. exactly corresponding to the true leakage distribution) with a Bayesian distinguisher. But since such perfect models are generally unknown, density estimation techniques have to be used to approximate the leakage distribution. This raises the fundamental problem that all security evaluations are potentially biased by both estimation and assumption errors. Hence, the best that we can hope is to be aware of these errors. In this paper, we provide and implement methodological tools to solve this issue. Namely, we show how sound statistical techniques allow both quantifying the leakage of a chip, and certifying that the amount of information extracted is close to the maximum value that would be obtained with a perfect model