In this paper we propose a Monte Carlo maximum likelihood estimation strategy for discretely observed Wright-Fisher diffusions. Our approach provides an unbiased estimator of the likelihood function and 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 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 numerical examples.

翻译：本文针对离散观测的莱特-费希尔扩散过程提出了一种蒙特卡洛最大似然估计策略。该方法提供了似然函数的无偏估计量，其基于精确模拟技术，对于定义在有界域上的扩散过程具有特殊价值——此类过程中数值方法通常难以保持在所需边界内。我们首先构建标量扩散的无偏似然估计量，随后将其推广至多维情形。文中同时给出了所提估计量的一致性理论结果，并通过数值算例展示了该方法的实际性能。