The infection fatality risk indicates the probability of death among infected individuals. The age-dependent heterogeneity of infection fatality risk is crucial for severity assessment and prioritization of countermeasures. However, infection fatality risk estimation requires infection data from a large-scale seroepidemiological survey combined with either direct ascertainment of deaths caused by infection or excess mortality estimates. To overcome the difficulty in ascertaining death, we propose an alternative approach to estimating the age-specific infection fatality risk for SARS-CoV-2 using medicolegal death investigation data in Tokyo with systematic post-mortem polymerase chain reaction testing. We integrated (i) polymerase chain reaction positivity among all deceased individuals at the Tokyo Medical Examiner’s Office, (ii) age-specific all-cause mortality risks from vital statistics, and (iii) age-stratified cumulative infection risks derived from seroepidemiological surveys. Infection fatality risk was computed using Bayes’ theorem. Results showed that infection fatality risk increased steeply with age. Our estimates (0.02% for ages 0–39 years, 0.30%–0.50% for ages 40–64 years, and 3.8%–4.2% for those aged ≥65 years) were consistent with published pre-vaccination meta-analytic estimates. Systematic testing within medicolegal death investigation systems can provide rapid, age-resolved severity assessments, improving the timeliness and comparability of infection fatality risk estimation across jurisdictions.

Several diagonal-based tail dependence indices have been suggested in the literature to quantify tail dependence. They have well-developed statistical inference theories but tend to underestimate tail dependence. For those problems when assessing the maximal strength of dependence is important (e.g., co-movements of financial instruments), the maximal tail dependence index was introduced, but it has so far lacked empirical estimators and statistical inference results, thus hindering its practical use. In the present paper, we suggest an empirical estimator for the index, explore its statistical properties, and illustrate its performance on simulated data.

Group judgements are often – implicitly or explicitly – influenced by their members’ individual expertise. However, given that expertise is seldom recognized fully and that some distortions may occur (bias, correlation, etc.), it is not clear that differential weighting is an epistemically advantageous strategy with respect to straight averaging. Our paper characterizes a wide set of conditions under which differential weighting outperforms straight averaging and embeds the results into the multidisciplinary group decision-making literature.

In practical applications of Credibility Theory the structure parameters usually have to be estimated from the data. This leads to an estimator of the a posteriori mean which is often biased and where the credibility factor depends on the data. A more coherent approach to the problem would be to also treat the unknown parameters as random variables and to simultaneously estimate the a posteriori mean and the structure parameters. Different statistical models are proposed which allow for such a solution. These models all lead to an estimation of the posterior mean which is a weighted average of the prior mean and of the observed mean, the weights depending on the observations.

Suppose two alternative designs for a stochastic system are to be compared. These two systems can be simulated independently or dependently. This paper presents a method for comparing two regenerative stochastic processes in a dependent fashion using common random numbers. A set of sufficient conditions is given that guarantees that the dependent simulations will produce a variance reduction over independent simulations. Numerical examples for a variety of simple stochastic models are included which illustrate the variance reduction achieved.