.png){kind=icon}
.png){kind=icon}
Return
Empirical Joint Estimation for Incubation Period and Infection Under the Doubly Interval-Censored with Right Truncated Observation
.png){kind=icon}
PRE
AI
A
S
DOI：10.1080/10618600.2026.2652940.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Doubly interval-censoring occurs frequently in infectious disease data, but the nonparametric estimators suitable for doubly interval-censored observed data are not well known. We develop a nonparametric estimator for the distribution function of doubly interval-censored data. Our proposed method analyzes the distributions of the incubation period and the infections simultaneously, and we derive its estimator based on the Bayesian and maximum likelihood methods. The simulation study suggests that this estimator is consistent. The usefulness of the proposed method is shown by a practical example using the reports of the novel coronavirus disease.
Keywords:
Empirical distribution
Non-homogeneous Poisson process
Nonparametric estimation
Survival analysis
Journal
J
IF:
1.8
Papers:
116
Citations:
6.4K
Organization
Cited Papers