In this paper we propose an unbiased Monte Carlo maximum likelihood estimator for discretely observed Wright-Fisher diffusions. Our approach is based on exact simulation techniques that are of special interest for diffusion processes defined on a bounded domain, where numerical methods typically fail to remain within the required boundaries. We start by building unbiased maximum likelihood estimators for scalar diffusions and later present an extension to the multidimensional case. Consistency results of our proposed estimator are also presented and the performance of our method is illustrated through a numerical example.

翻译：本文提出了一种针对离散观测的Wright-Fisher扩散过程的无偏蒙特卡罗极大似然估计方法。该方法基于精确模拟技术，对于定义在有界域上的扩散过程具有特殊意义——传统数值方法通常难以保证结果保持在所需边界内。我们首先构建标量扩散过程的无偏极大似然估计，随后将其推广至多维情形。本文还给出了所提估计量的一致性结果，并通过数值算例展示了该方法的性能。