The problem of estimating the period of a periodic point process is considered when the observations are sparse and noisy. There is a class of estimators that operate by maximizing an objective function over an interval of possible periods, notably the periodogram estimator of Fogel & Gavish [1] and the line-search algorithms of Sidiropoulos et al. [2] and Clarkson [3]. For numerical calculation, the interval is sampled. However, it is not known how fine the sampling must be in order to ensure statistically accurate results. In this paper, a new estimator is proposed which eliminates the need for sampling. For the proposed statistical model, it calculates a maximumlikelihood estimate. It is shown that the expected arithmetic complexity of the algorithm is O(n 3 log n) where n is the number of observations. Numerical simulations demonstrate the superior statistical performance of the new estimator.

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