AI 中文总结
该研究提出基于参数模型的高维协方差矩估计方法，在定量遗传学中实现为mlmm-mom包，可适配多种常见实验设计以估计遗传协方差分量。
AI 中文摘要
我们针对多元混合效应模型中方差分量协方差矩阵的特征值，提出了矩估计方法。假设特征值分布服从参数形式，我们聚焦于高维情形，其中预测变量数量庞大，且与每个随机效应的实现数量相当。在该设定下，我们证明了平方和矩阵的经验矩（例如协方差矩阵的MANOVA估计量）可由底层参数的确定性函数紧密近似。该关系可通过矩匹配构造出一致且渐近正态的估计量。我们的方法受定量遗传学应用驱动，在该领域中，估计多个表型性状间的遗传协方差分量是核心研究内容。我们在新的Python包mlmm-mom中实现了该方法，并展示了其如何适配该领域的多种常见实验设计。
英文摘要
We propose method-of-moments estimators for the eigenvalues of variance component covariance matrices in multivariate mixed effects models. Assuming a parametric form for the eigenvalue distribution, we focus on the high-dimensional regime where the number of predictors is large and comparable to the number of realizations of each random effect. In this setting, we show that the empirical moments of sum-of-squares matrices (e.g., MANOVA estimators of the covariance matrices) can be closely approximated by deterministic functions of the underlying parameters. This relationship enables the construction of consistent and asymptotically normal estimators via moment matching. Our approach is motivated by applications in quantitative genetics, where estimating genetic covariance components across multiple phenotypic traits is of central interest. We implement our method in a new python package mlmm-mom, and demonstrate how our method adapts to several common experimental designs in this domain.
Comments52 pages, 16 figures
