论文标题
孵育时间分布的非参数估计
Nonparametric estimation of the incubation time distribution
论文作者
论文摘要
在反问题中，非参数最大似然估计器（MLE）通常具有非正常的极限分布，例如Chernoff的分布。但是，如果一个人认为模型的平滑功能，具有MLE的相应功能，则可以获得正常的限制分布和更快的收敛速率。我们为疾病的孵育时间模型证明了这一点。后者模型中通常的方法是使用参数分布，例如Weibull和Gamma分布，这会导致估计器不一致。讨论了用于构建置信区间的平滑引导方法。在这种情况下，基于非参数MLE本身的经典引导程序已被证明是不一致的。
Nonparametric maximum likelihood estimators (MLEs) in inverse problems often have non-normal limit distributions, like Chernoff's distribution. However, if one considers smooth functionals of the model, with corresponding functionals of the MLE, one gets normal limit distributions and faster rates of convergence. We demonstrate this for a model for the incubation time of a disease. The usual approach in the latter models is to use parametric distributions, like Weibull and gamma distributions, which leads to inconsistent estimators. Smoothed bootstrap methods are discussed for constructing confidence intervals. The classical bootstrap, based on the nonparametric MLE itself, has been proved to be inconsistent in this situation.
