试论秦汉初历法的置闰规则

从史书和历简来看,秦至汉初的后九月存在置于无中气之月所在年和上一年两种情况,因而其历法置闰不是简单的归余于终.论者以为这是采取将某一节气恒出现在某月作为置闰之原则,亦即固定节气置闰法,但今出历简存在明显的反证.根据史载和出土历简很容易将这一时段的所有闰年排出,从而说明了这一时期的置闰遵循一个既定的经验序列.     

Estimation of Continuous‐Time Markov Processes Sampled at Random Time Intervals

We introduce a family of generalized‐method‐of‐moments estimators of the parameters of a continuous‐time Markov process observed at random time intervals. The results include strong consistency, asymptotic normality, and a characterization of standard errors. Sampling is at an arrival intensity that is allowed to depend on the underlying Markov process and on the parameter vector to be estimated. We focus on financial applications, including tick‐based sampling, allowing for jump diffusions, regime‐switching diffusions, and reflected diffusions.     

Analyzing factors contributing to COVID-19 mortality in the United States using artificial intelligence techniques

Having started since late 2019, COVID-19 has spread through far many nations around the globe. Not being known profoundly, the novel virus of the Coronaviruses family has already caused more than half a million deaths and put the lives of many more people in danger. Policymakers have implemented preventive measures to curb the outbreak of the virus, and health practitioners along with epidemiologists have pointed out many social and hygienic factors associated with the virus incidence and mortality. However, a clearer vision of how the various factors cited hitherto can affect total death in different communities is yet to be analyzed. This study has put this issue forward. Applying artificial intelligence techniques, the relationship between COVID-19 death toll and determinants mentioned as strongly influential in earlier studies was investigated. In the first stage, employing Best-Worst Method, the weight of the primer contributing factor, effectiveness of strategies, was estimated. Then, using an integrated Best-Worst Method–local linear neuro-fuzzy–adaptive neuro-fuzzy inference system approach, the relationship between COVID-19 mortality rate and all factors namely effectiveness of strategies, age pyramid, health system status, and community health status was elucidated more specifically.     

社区公共服务对老年人幸福感的影响

本文基于中国老年健康影响因素跟踪调查(CLHLS)2018年数据,运用Ordered probit模型和工具变量条件混合过程估计法(CMP方法)考察社区公共服务对老年人幸福感的影响。研究表明,社区公共服务完善程度对老年人幸福感具有显著的促进作用,且这一影响具有明显的城乡、独居养老成因和年龄异质性。社区公共服务匹配度对社区公共服务的老年幸福感效应具有明显调节作用:社区公共服务匹配度越高,社区公共服务对老年人幸福感的促进作用越强。因此,构建以社区为依托的多层次养老体系不仅要着力完善社区公共服务,更要重视社区公共服务供给与老年居民真实需求间的精准匹配,以在提升资源利用效率的同时更好地发挥社区公共服务对老年居民福祉的促进作用。     

Measuring economic competence of youth with a short scale

We present a 12-item scale measuring the cognitive component of economic competence and document the psychometric properties of the scale. Using a data set with >12,000 secondary school students in Germany, the scale shows high discriminatory power and covers a wide range of ability levels. Analyses of ‘Differential Item Functioninǵ show no item bias across key demographic characteristics, and scores show meaningful associations with scores obtained from adjacent test instruments. Student-level correlates mirror estimates documented in earlier literature as well as results relying on a more extensive version of the scale with 31 items. The presented short scale enables researchers and practitioners to efficiently measure economic competence of youth.     

Name order effects in measuring adolescent social networks using rosters

Recent studies have found order effects in social network data collection, where later names on a roster receive fewer nominations. Some thus argue for randomizing name orders or sampling peer names for survey rosters. We model order effects as biases in nomination choices and demonstrate observational and experimental methods for assessing these biases and illuminating their mechanisms. Employing these lenses, we find little evidence of order effects on eight sociometric questions in four middle school cohorts over six waves. To inform future work, we investigate aspects of the survey situation that may amplify or attenuate order effects. Analyzing these moderating forces offers guidance for detecting, understanding, and mitigating order effects in future research.