  Assume that we observe a large number of curves, 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 the elapsed time
between repetitive pulses with a possibly low signal-noise ratio, and without a
knowledge of the pulse shape is of interest. We suggest an M-estimator leading
to a three-stage algorithm: we split our data set in blocks, on which the
estimation of the shifts is done by minimizing a cost criterion based on a
functional of the periodogram; the estimated shifts are then plugged into a
standard density estimator. We show that under mild regularity assumptions the
density estimate converges weakly to the true shift distribution. The theory is
applied both to simulations and to alignment of real ECG signals. The estimator
of the shift distribution performs well, even in the case of low
signal-to-noise ratio, and is shown to outperform the standard methods for
curve alignment.
