基于拉盖尔多项式的潜伏期与代际时间半参数估计
Semi-Parametric Estimation of Incubation and Generation Times by Means of Laguerre Polynomials
AI总结：
针对传染病潜伏期与代际时间分布难以精确观测估计的问题，提出基于拉盖尔多项式的半参数筛估计方法，证明其一致性并通过模拟验证小样本性能。
摘要：
在传染病流行中，许多关键指标（如再生数）都依赖于潜伏期（从感染到症状出现的时间）和/或代际时间（从一名感染者感染到另一个人被感染的时间）。因此，估计这两个量的分布具有重要研究价值。但由于通常无法获得这两个变量的精确观测值，该问题颇具挑战性。而在大流行初期，可观测到感染对中两人的症状出现时间，以及首例感染者的感染时间窗口（例如因前往风险地区而明确的窗口）。本文提出一种基于Laguerre-Polynomials（拉盖尔多项式）的简单半参数筛估计方法，用于估计上述分布。文中给出了估计一致性的详细理论证明，并通过模拟研究验证了该方法在小数据集下的有限样本性能。
英文摘要：
In epidemics many interesting quantities, like the reproduction number, depend on the incubation period (time from infection to symptom onset) and/or the generation time (time until a new person is infected from another infected person). Therefore, estimation of the distribution of these two quantities is of distinct interest. However, this is a challenging problem since it is normally not possible to obtain precise observations of these two variables. Instead, in the beginning of a pandemic, it is possible to observe for infection pairs the time of symptom onset for both people as well as a window for infection of the first person (e.g. because of travel to a risk area). In this paper we suggest a simple semi-parametric sieve-estimation method based on Laguerre-Polynomials for estimation of these distributions. We provide detailed theory for consistency and illustrate the finite sample performance for small datasets via a simulation study.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
