标题： 离散观测的线性出生-死亡过程的近似最大似然估计量 显示英文标题

标题： An Approximate Maximum Likelihood Estimator for Discretely Observed Linear Birth-and-Death Processes

摘要： 线性出生-死亡过程（LBDPs）是种群动态、进化生物学和造血作用中的基础随机模型。 由于不规则采样、噪声和缺失值，从离散观测数据中估计参数在计算上是耗时的。 我们提出了一种新的近似最大似然估计器（MLE），基于对转移概率的高斯近似。 该方法将估计转化为一个单变量优化问题，在不牺牲准确性的情况下实现了显著的计算优势。 通过模拟，我们表明在现实噪声和稀疏性条件下，近似MLE在速度和精度上优于高斯和鞍点基估计器。 应用于纵向克隆造血数据，该方法即使在噪声和组成输入的情况下也能产生生物上有意义的增长估计。 与高斯和鞍点近似不同，我们的估计器对数据缩放是不变的，使其成为真实世界应用（如变异等位基因频率分析）的理想选择。 显示英文摘要

摘要： Linear birth-and-death processes (LBDPs) are foundational stochastic models in population dynamics, evolutionary biology, and hematopoiesis. Estimating parameters from discretely observed data is computationally demanding due to irregular sampling, noise, and missing values. We propose a novel approximate maximum likelihood estimator (MLE) for LBDPs based on a Gaussian approximation to transition probabilities. The approach transforms estimation into a univariate optimization problem, achieving substantial computational gains without sacrificing accuracy. Through simulations, we show that the approximate MLE outperforms Gaussian and saddlepoint-based estimators in speed and precision under realistic noise and sparsity. Applied to longitudinal clonal hematopoiesis data, the method produces biologically meaningful growth estimates even with noisy, compositional input. Unlike Gaussian and saddlepoint approximations, our estimator is invariant to data scaling, making it ideal for real-world applications such as variant allele frequency analyses.