Estimation of the incubation time distribution in the singly and doubly interval censored model

TL;DR

This paper develops nonparametric estimators for the incubation time distribution in interval censored models, providing more data-aligned estimates and explicit limit distributions for confidence intervals, enhancing existing parametric approaches.

Contribution

It introduces nonparametric estimation methods for incubation time distributions in interval censored models, offering explicit limit distributions and computational tools.

Findings

Nonparametric estimates are closer to data than parametric methods.

Explicit limit distributions for discrete models are derived.

R scripts for estimation are provided.

Abstract

We analyze nonparametric estimators for the distribution function of the incubation time in the singly and doubly interval censoring model. The classical approach is to use parametric families like Weibull, log-normal or gamma distributions in the estimation procedure. We propose nonparametric estimates which stay closer to the data than the classical parametric methods. We also give explicit limit distributions for discrete versions of the models and apply this to compute confidence intervals. The methods complement the analysis of the continuous model. R scripts for computation of the estimates are provided on https://github.com/pietg/incubationtime.