Reducing Bias in Event Time Simulations via Measure Changes
针对事件时间随机点过程模型的蒙特卡洛模拟中标准算法产生有偏估计的问题，提出一种概率测度变换方法，可减少甚至消除偏差且不限制算法适用范围，并给出保证点过程鞅存在的新条件。
Stochastic point process models of event timing are common in many areas, including finance, insurance, and reliability. Monte Carlo simulation is often used to perform computations for these models. The standard sampling algorithm, which is based on a time-change argument, is widely applicable but generates biased simulation estimators. This article develops and analyzes a change of probability measure that can reduce or even eliminate the bias without restricting the scope of the algorithm. A result of independent interest offers new conditions that guarantee the existence of a broad class of point process martingales inducing changes of measure. Numerical results illustrate our approach.