Theoretical results on the discrete Weibull distribution of Nakagawa and Osaki

In this paper, we discuss some theoretical results and properties of the discrete Weibull distribution, which was introduced by Nakagawa and Osaki [The discrete Weibull distribution. IEEE Trans Reliab. 1975;24:300–301]. We study the monotonicity of the probability mass, survival and hazard functions. Moreover, reliability, moments, p-quantiles, entropies and order statistics are also studied. We consider likelihood-based methods to estimate the model parameters based on complete and censored samples, and to derive confidence intervals. We also consider two additional methods to estimate the model parameters. The uniqueness of the maximum likelihood estimate of one of the parameters that index the discrete Weibull model is discussed. Numerical evaluation of the considered model is performed by Monte Carlo simulations. For illustrative purposes, two real data sets are analyzed.     

考虑项目关联关系的R&D网络风险传播建模与仿真

考虑同一企业内不同项目间关联关系的情况下，基于研发项目的数量将研发网络分为不同子网络。借鉴SIS模型构建了R&D网络风险传播动力学模型，并对该模型进行了数理解析与仿真分析。结果表明：R&D网络风险传播阈值是由不同子网络的拓扑结构以及企业内不同项目间的关联程度共同决定的；考虑项目关联的研发网络风险传播阈值小于不考虑关联关系的研发网络风险传播阈值；项目间关联关系的企业个数及项目间的关联程度对风险在不同子网络上的传播速度与传播范围均具有影响。该研究成果对于提高研发网络抗风险能力具有重要意义。     

半参数空间ZISF的估计、分类与蒙特卡罗模拟

零无效率随机前沿模型（ZISF）包含随机前沿模型和回归模型，两模型各有一定的发生概率，适用于技术无效生产单元和技术有效生产单元同时存在的情形。本文在ZISF的生产函数中引入空间效应和非参函数，并假设回归模型的发生概率为非参函数，构建了半参数空间ZISF。该模型可有效避免忽略空间效应导致的有偏且不一致估计量，也避免了线性模型的拟合不足。本文对非参函数采用B样条逼近，使用极大似然方法和JLMS法分别估计参数和技术效率。蒙特卡罗结果表明：①本文方法的估计精度和分类精度均较高。随着样本容量的增大，精度增加。②忽略空间效应或者非参数效应，估计精度和分类精度降低，文中模型有存在必要性。③忽略发生概率的非参数效应会严重降低估计和分类精度，远大于忽略生产函数的非参数效应的影响。     

Modeling stereotypes and negative self‐stereotypes as a function of interactions among groups with power asymmetries

Stereotypes is one of the most researched topics in social psychology. Within this context, negative self‐stereotypes pose a particular challenge for theories. In the current work, we propose a model that suggests that negative self‐stereotypes can theoretically be accounted for by the need to communicate in a social system made up by groups with unequal power. Because our theory is dynamic, probabilistic, and interactionist, we use a computational simulation technique to show that the proposed model is able to reproduce the phenomenon of interest, to provide novel accounts of related phenomena, and to suggest novel empirical predictions. We describe our computational model, our variables' dynamic behavior and interactions, and link our analyses to the literature on stereotypes and self‐stereotypes, the stability of stereotypes (in particular, gender and racial stereotypes), the effects of power asymmetries, and the effects of intergroup contact.     

Translating cross-lagged effects into incidence rates and risk ratios: The case of psychosocial safety climate and depression

Longitudinal studies are the gold standard of empirical work and stress research whenever experiments are not plausible. Frequently, scales are used to assess risk factors and their consequences, and cross-lagged effects are estimated to determine possible risks. Methods to translate cross-lagged effects into risk ratios to facilitate risk assessment do not yet exist, which creates a divide between psychological and epidemiological work stress research. The aim of the present paper is to demonstrate how cross-lagged effects can be used to assess the risk ratio of different levels of psychosocial safety climate (PSC) in organisations, an important psychosocial risk for the development of depression. We used available longitudinal evidence from the Australian Workplace Barometer (N?=?1905) to estimate cross-lagged effects of PSC on depression. We applied continuous time modelling to obtain time-scalable cross effects. These were further investigated in a 4-year Monte Carlo simulation, which translated them into 4-year incident rates. Incident rates were determined by relying on clinically relevant 2-year periods of depression. We suggest a critical value of PSC?=?26 (corresponding to ?1.4 SD), which is indicative of more than 100% increased incidents of persistent depressive disorder in 4-year periods compared to average levels of PSC across 4 years.     

政府奖惩机制下药品安全质量监管三方演化博弈及仿真分析

药品安全关乎人民群众生命健康、经济发展、社会稳定和国家安全，一直以来都是政府和全社会关注的热点问题之一。但是，近年来频发的各类药品安全事件，揭露了政府监管缺失、药品检测机制不完善、公众投诉失效等诸多问题。本文考虑了药品检测中存在寻租现象，构建了药品生产企业、第三方药品检测机构和政府监管部门之间的三方演化博弈模型，分析了各参与方策略选择的演化稳定性，探讨了各要素对三方策略选择的影响关系，进一步分析了三方博弈系统中均衡点的稳定性。研究结果表明：1)政府增强奖励、惩罚力度均有助于促进企业生产高药效药品、第三方药品检测机构拒绝寻租的规范行为，但是增大奖励力度将不利于政府自身履行监管职责；2)政府设定合理的奖惩机制必须符合对各方的奖惩之和大于其投机收益的条件，才能保障演化稳定市场环境下的药品安全；3)上级政府对监管部门失职的问责对增强企业生产高药效药品的稳健性具有重要意义；4)提升企业的药品销售收益、增加企业寻租成本也是避免企业生产低药效药品的有效途径。最后，利用Matlab 2020b进行了仿真分析，为政府完善药品监管机制提出了对策与建议。     

The Times They are A’changing: Multigenerational Family Simulations

Multigenerational households are increasingly affecting both the individual and family as well as community organizations and social policies. Social work and other family studies students can profit from educational modalities that use adult learning applications through a systems life-course perspective, the whole family aging over time. Family simulation software—addressing multigenerational families, such as two or more adult generations living together—builds on a previous paper (Marriage & Family Review, Feb. 2015). Social class, among other demographic and environmental variables, is emphasized. Agent-based family social network simulation of multigenerational families can facilitate experiential learning. An automatically generated life events report, based on both factual data and specific family characteristics, can be used as a classroom case study for role playing and assessing.     

基于交叉效率DEA与群体共识的区间乘性语言偏好关系群决策

针对区间乘性语言偏好关系群决策问题，提出了一种基于交叉效率DEA和群体共识的群决策方法。首先，提出乘性语言偏好关系导出函数的定义，并构建产出导向的DEA模型，证明了一致性乘性语言偏好关系的DEA效率得分与排序向量之间存在比例关系。在此基础上，建立基于理想值的交叉效率DEA模型，提出乘性语言偏好关系的通用排序方法。同时，基于群体共识建立目标规划模型来计算各语言偏好关系的权重系数。最后，利用Monte Carlo随机模拟的方法对群体语言偏好空间进行统计分析，得到群决策期望排序向量及其可信度。算例分析表明本文方法能够有效的避免信息损失，具有较强的适用性和较高的可信度。     

A non‐stationary bivariate INAR(1) process with a simple cross‐dependence: Estimation with some properties

This paper considers modelling of a non‐stationary bivariate integer‐valued autoregressive process of order 1 (BINAR(1)) where the cross‐dependence between the counting series is formed through the relationship of the current series with the previous‐lagged count series observations while the pair of innovations is independent and marginally Poisson. In addition, this paper proposes a generalised quasi‐likelihood (GQL) estimating equation based on the exact specification of the mean score and the auto‐covariance structure. The proposed approach is also compared with other popular techniques such as conditional maximum likelihood (CML), generalised least squares (GLS) and generalised method of moment (GMM) based on simulated data from the proposed BINAR(1). Moreover, the model is applied to weekly series of day and night road accidents arising in some regions of Mauritius and is compared with other existing BINAR(1) models.