Uri Isserles

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In this paper we address a problem related to curve alignment with a semiparametric framework, that is without any knowledge of the shape. This problem appears in many biological applications, in which we are interested in the estimation of the elapsed duration distribution between two signals, but wish to estimate it with a possibly low signal-noise ratio,(More)
We observe a large number of signals, all of them with identical, although unknown, shape, but with a different random shift. The objective is to estimate the individual time shifts and their distribution. Such an objective appears in several biological applications like neuroscience or ECG signal processing, in which the estimation of the distribution of(More)
We address the problem of curve alignment with a semiparametric framework, that is without any knowledge of the shape. This study stems from a biological issue, in which we are interested in the estimation of the average heart cycle signal, but wish to estimate it without any knowledge of the pulse shape, which may differ from one patient to another. We(More)
Suppose we observe a large number of curves, all with identical, although unknown, shape, but with a different random shift. The objective is to estimate the individual time shifts and their distribution. Such an objective appears in several biological applications, in which the interest is in the estimation of the distribution of the elapsed time between(More)
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