A Bayesian Analysis of Abundance,Trend, and Population Viability for Harbor Seals in Iliamna Lake,Alaska

Harbor seals in Iliamna Lake, Alaska, are a small, isolated population, and one of only two freshwater populations of harbor seals in the world, yet little is known about their abundance or risk for extinction. Bayesian hierarchical models were used to estimate abundance and trend of this population. Observational models were developed from aerial survey and harvest data, and they included effects for time of year and time of day on survey counts. Underlying models of abundance and trend were based on a Leslie matrix model that used prior information on vital rates from the literature. We developed three scenarios for variability in the priors and used them as part of a sensitivity analysis. The models were fitted using Markov chain Monte Carlo methods. The population production rate implied by the vital rate estimates was about 5% per year, very similar to the average annual harvest rate. After a period of growth in the 1980s, the population appears to be relatively stable at around 400 individuals. A population viability analysis assessing the risk of quasi‐extinction, defined as any reduction to 50 animals or below in the next 100 years, ranged from 1% to 3%, depending on the prior scenario. Although this is moderately low risk, it does not include genetic or catastrophic environmental events, which may have occurred to the population in the past, so our results should be applied cautiously.     

A New Methodology for Before–After Safety Assessment Using Survival Analysis and Longitudinal Data

The widely used empirical Bayes (EB) and full Bayes (FB) methods for before–after safety assessment are sometimes limited because of the extensive data needs from additional reference sites. To address this issue, this study proposes a novel before–after safety evaluation methodology based on survival analysis and longitudinal data as an alternative to the EB/FB method. A Bayesian survival analysis (SARE) model with a random effect term to address the unobserved heterogeneity across sites is developed. The proposed survival analysis method is validated through a simulation study before its application. Subsequently, the SARE model is developed in a case study to evaluate the safety effectiveness of a recent red‐light‐running photo enforcement program in New Jersey. As demonstrated in the simulation and the case study, the survival analysis can provide valid estimates using only data from treated sites, and thus its results will not be affected by the selection of defective or insufficient reference sites. In addition, the proposed approach can take into account the censored data generated due to the transition from the before period to the after period, which has not been previously explored in the literature. Using individual crashes as units of analysis, survival analysis can incorporate longitudinal covariates such as the traffic volume and weather variation, and thus can explicitly account for the potential temporal heterogeneity.     

制造企业生存和发展的推动力--技术创新

技术创新是制造企业发展的原动力和核心竞争力.培养员工的创新精神,营造有利于创新的企业文化环境,制定符合制造企业发展要求的创新策略,建立鼓励创新的管理机制和创新能力评价体系是提高制造企业创新能力的关键.     

一个中国知识分子的仕士适——《顾准文集》读后

通过顾准的人生境遇分析中国知识分子为官、为儒的命运 ,提出 :官有官道 ,文有文道 ;当代中国知识分子有追求 ,更有窘迫 ;思想大师来自于苦难和冷清 ;思想让人成为人     

论宪政生存的经济条件

自然经济和计划经济不可能产生宪政。市场经济是宪政产生和发展的前提和基础。市场经济产生权利观念、权利意识,促进政治法治化、民主化,要求限制政府权力,保护公民权利,产生宪法观念和宪法,培育宪政生存的社会基础———市民社会。     

Estimation of dependence between paired correlated failure times in the presence of covariate measurement error

Summary. In many biomedical studies, covariates are subject to measurement error. Although it is well known that the regression coefficients estimators can be substantially biased if the measurement error is not accommodated, there has been little study of the effect of covariate measurement error on the estimation of the dependence between bivariate failure times. We show that the dependence parameter estimator in the Clayton–Oakes model can be considerably biased if the measurement error in the covariate is not accommodated. In contrast with the typical bias towards the null for marginal regression coefficients, the dependence parameter can be biased in either direction. We introduce a bias reduction technique for the bivariate survival function in copula models while assuming an additive measurement error model and replicated measurement for the covariates, and we study the large and small sample properties of the dependence parameter estimator proposed.     

A Multivariate Model for Repeated Failure Time Measurements

A parametric multivariate failure time distribution is derived from a frailty-type model with a particular frailty distribution. It covers as special cases certain distributions which have been used for multivariate survival data in recent years. Some properties of the distribution are derived: its marginal and conditional distributions lie within the parametric family, and association between the component variates can be positive or, to a limited extent, negative. The simple closed form of the survivor function is useful for right-censored data, as occur commonly in survival analysis, and for calculating uniform residuals. Also featured is the distribution of ratios of paired failure times. The model is applied to data from the literature     

生存理性如何影响农户的差别化生产

在食品安全威胁下,农户重新定义了“为自家生产”和“为市场生产”的差别化生产行为。基于经济理性行为假设认为农户存在以确保自家食品安全为动机的生存理性。根据在5省区收集的827份农村入户调研数据,对农户差别化生产行为进行具体分析,描述了差别化生产行为总体特征,并测量农户食品安全自我保护意识、威胁感知程度和信息渠道等因素。运用Probit模型验证了命题：以食品安全自我保护意识为特征的生存理性,支配了农户为确保自家消费安全而进行的差别化生产。     

从苏童的《妻妾成群》探究中国的女性权力问题

本文分析苏童小说《妻妾成群》,深刻地揭示了女性生存权力的抗争。     

Survival in the U.S. petroleum refining industry

Of the 324 petroleum refineries operating in the U.S. in 1982, only 149 were still in the hands of their original owners in 2007. Using duration analysis, this paper explores why refineries change ownership or shut down. Plants are more likely to ‘survive’ with their original owners if they are older or larger, but less likely if the owner is a major integrated firm, or the refinery is a more technologically complex one. This latter result differs from existing research on the issue. This paper also presents a split population model to relax the general assumption of the duration model that all refiners will eventually close down; the empirical results show that the split population model converges on a standard hazard model; the log-logistic version fits best. Finally, a multinomial logit model is estimated to analyze the factors that influence the refinery plant's choices of staying open, closing, or changing ownership. Plant size, age and technology usage have positive impacts on the likelihood that a refinery will stay open, or change ownership (rather than close down).