Increase in common longevity and the compression of mortality: the case of Japan

This study shows a strong increase in the modal age at death (M) in Japan over a period of 50 calendar years, accompanied by a clear decrease in the standard deviation of ages at death above M (SD(M+)) until the 1990s for men and the mid-1980s for women. For the most recent periods SD(M+) appears to have stopped decreasing, even though M has continued to increase linearly. This stagnation in SD(M+) has been accompanied by stagnation in q(M). The number of deaths at M (d(M)) and the number of deaths at and above M (d(M+)) have increased, but significantly more slowly since the period 1975-79. Since the 1980s an acceleration in the increase of M+kSD(M+), our indicator of the longest life durations, has been essentially due to the pause in SD(M+). Our data do not suggest that we are approaching an upper limit in human longevity.     

Modeling the detection rates of fires in nuclear plants: development and application of a methodology for treating imprecise evidence

A model is developed for the detection time of fires in nuclear power plants, which differentiates between competing modes of detection and between different initial fire severities. Our state-of-knowledge uncertainties in the values of the model parameters are assessed from industry experience using Bayesian methods. Because the available data are sparse, we propose means to interpret imprecise forms of evidence to the develop quantitative information, which can be used in a statistical analysis; the intent is to maximize our use of all available information. Sensitivity analyses are performed to indicate the importance of structural and distributional assumptions made in the study. The methods used to treat imprecise evidence can be applied to a wide variety of problems. The specific equations developed in this analysis are useful in general situations, where the random quantity of interest is the minimum of a set of random variables (e.g., in "competing risks" models). The computational results indicate that the competing modes formulation can lead to distributions different from those obtained via analytically simpler models, which treat each mode independently of the others.     

Sample Size Calculation for Rank Tests Comparing K Survival Distributions

Rank tests, such as logrank or Wilcoxon rank sum tests, have been popularly used to compare survival distributions of two or more groups in the presence of right censoring. However, there has been little research on sample size calculation methods for rank tests to compare more than two groups. An existing method is based on a crude approximation, which tends to underestimate sample size, i.e., the calculated sample size has lower power than projected. In this paper we propose an asymptotically correct method and an approximate method for sample size calculation. The proposed methods are compared to other methods through simulation studies.     

Engaging with happy-sounding music promotes helping behavior in 18-month-olds

Engaging with music fosters prosocial responding in infants and toddlers. In this pilot study, we examined whether music that expresses contrasting emotions (happy vs. sad) was associated with toddlers’ helpfulness. Seventy-five 18-month-olds from Hong Kong China were randomly assigned to engage with music with an experimenter in one of two conditions: happy- or sad-sounding music. After the musical engagement task, toddlers from both conditions completed the same set of helping tasks. For instrumental (action-based) helping, toddlers were significantly more helpful after engaging with happy-sounding music than with sad-sounding music. Our initial findings suggest that cues linked to happy- and sad-sounding music influence toddlers’ prosocial responses.     

Portfolio value-at-risk estimation for spot chartering decisions under changing trade patterns: A copula approach

Evolving geopolitical relationships between countries (especially between China and the United States) in recent years have highlighted dynamically changing trade patterns across the globe, all of which elevate risk and uncertainty for transport service providers. In order to mitigate risks, shipowners and operators must be able to estimate risks appropriately; one potentially promising method of doing so is through the value-at-risk (VaR) method. VaR describes the worst loss a portfolio is likely to sustain, which will not be exceeded over a target time horizon at a given level of confidence. This article proposes a copula-based GARCH model to estimate the joint multivariate distribution, which is a key component in VaR estimation. We show that the copula model can capture the VaR more successfully, as compared with the traditional method of calculation. As an empirical study, the expected portfolio VaR is examined when a shipowner chooses among Panamax soybean trading routes under a condition of reduced trade volumes between the United States and China due to the ongoing trade turmoil. This study serves as one of the very few papers in the literature on shipping portfolio VaR analysis. The results have significant implications for shipowners regarding fleet repositioning, decision making, and risk management.