Weighted Estimator for the Linear Transformation Models with Multivariate Failure Time Data

In this article, a simple and efficient weighted method is proposed to improve the estimation efficiency for the linear transformation models with multivariate failure time data. Asymptotic properties of the estimators with a closed-form variance-covariance matrix are established. In addition, a goodness-of-fit test is developed to evaluate the adequacy of the model. The performance of proposed method and the comparison on the efficiency between the proposed method and the working independence method (Lu, 2005) are conducted in finite-sample situation by simulation studies. Finally a real data set from the Busselton Population Health Surveys is illustrated to validate the proposed methodology. The related proofs of the theorems are given in the Appendix.     

A statistical social network model for consumption data in trophic food webs

We adapt existing statistical modeling techniques for social networks to study consumption data observed in trophic food webs. These data describe the feeding volume (non-negative) among organisms grouped into nodes, called trophic species, that form the food web. Model complexity arises due to the extensive amount of zeros in the data, as each node in the web is predator/prey to only a small number of other trophic species. Many of the zeros are regarded as structural (non-random) in the context of feeding behavior. The presence of basal prey and top predator nodes (those who never consume and those who are never consumed, with probability 1) creates additional complexity to the statistical modeling. We develop a special statistical social network model to account for such network features. The model is applied to two empirical food webs; focus is on the web for which the population size of seals is of concern to various commercial fisheries.     

Characterizations of Bivariate Models Using Some Dynamic Conditional Information Divergence Measures

In this article, we study some relevant information divergence measures viz. Renyi divergence and Kerridge’s inaccuracy measures. These measures are extended to conditionally specified models and they are used to characterize some bivariate distributions using the concepts of weighted and proportional hazard rate models. Moreover, some bounds are obtained for these measures using the likelihood ratio order.     

About an adaptively weighted Kaplan-Meier estimate

The minimum averaged mean squared error nonparametric adaptive weights use data from m possibly different populations to infer about one population of interest. The definition of these weights is based on the properties of the empirical distribution function. We use the Kaplan-Meier estimate to let the weights accommodate right-censored data and use them to define the weighted Kaplan-Meier estimate. The proposed estimate is smoother than the usual Kaplan-Meier estimate and converges uniformly in probability to the target distribution. Simulations show that the performances of the weighted Kaplan-Meier estimate on finite samples exceed that of the usual Kaplan-Meier estimate. A case study is also presented.     

以跨文化视角审视纳博科夫的文学世界

俄裔美籍双语作家、文艺评论家兼诗人弗拉基米尔·纳博科夫,在20世纪世界文学史上占有重要的位置.他一生辗转迁居,造就了他跨国界、跨民族、跨语言、跨文化的多彩文学艺术个性,即在浓郁的俄罗斯文化背景上叠印了英美社会现实的缩影.跨文化的创作就需要我们用跨文化的视角去审视、去衡量、去解读,这样才能给予他合理而正确的评价,也才能真正走入纳博科夫具有多彩艺术个性的文学世界.     

刘易斯拐点对甘肃地区“民工荒”现象的验证分析

近年甘肃地区出现了＂民工荒＂现象,是否意味着刘易斯拐点的到来？通过对二元经济模型和刘易斯拐点的理论进行重新梳理,确定拐点的判断标准,即劳动力转移引起农业部门制度工资上涨,进而引起工业部门实际工资上涨。结合甘肃地区实际情况进行分析结果表明,甘肃地区出现的＂民工荒＂不能认为是刘易斯拐点的到来。解决＂民工荒＂的对策应该是改善农民工就业环境;加大就业技能培训,提高农民工科技文化素质;提供优惠政策,鼓励农民工回乡创业。     

介质中点电荷所受电场力的研究

研究了一般情况下,在无限均匀介质中,点电荷所受到的电场力与在真空中一样,满足库仑定律,并研究了介质中点电荷所受电场力满足库仑定律的充要条件．     

我国高等教育开展创新教育的突破口

二十一世纪,世界各国的竞争主要是科学技术的竞争。科学技术竞争的实质是创新人才的数量和质量的竞争。创新人才是具有创新技术的人才。创新技术的获得靠传授和学习。因此,开展创新教育,开设《创新技术》课,让学生学会掌握和运用创新技术,为社会经济技术发展培养大批量、高素质的创新人才,是二十一世纪世界发展对各国高等教育的必然要求,是我国高等教育目前刻不容缓的战略性任务。我国高等教育开展创新教育,培养大批量、高素质创新人才的突破口是编写和出版《创新技术》教材。     

Parameter estimation procedures for exponential-family random graph models on count-valued networks: A comparative simulation study

The exponential-family random graph models (ERGMs) have emerged as an important framework for modeling social networks for a wide variety of relational types. ERGMs for valued networks are less well-developed than their unvalued counterparts, and pose particular computational challenges. Network data with edge values on the non-negative integers (count-valued networks) is an important such case, with examples ranging from the magnitude of migration and trade flows between places to the frequency of interactions and encounters between individuals. Here, we propose an efficient parallelizable subsampled maximum pseudo-likelihood estimation (MPLE) scheme for count-valued ERGMs, and compare its performance with existing Contrastive Divergence (CD) and Monte Carlo Maximum Likelihood Estimation (MCMLE) approaches via a simulation study based on migration flow networks in two U.S. states. Our results suggest that edge value variance is a key factor in method performance, while network size mainly influences their relative merits in computational time. For small-variance networks, all methods perform well in point estimations while CD greatly overestimates uncertainties, and MPLE underestimates them for dependence terms; all methods have fast estimation for small networks, but CD and subsampled multi-core MPLE provides speed advantages as network size increases. For large-variance networks, both MPLE and MCMLE offer high-quality estimates of coefficients and their uncertainty, but MPLE is significantly faster than MCMLE; MPLE is also a better seeding method for MCMLE than CD, as the latter makes MCMLE more prone to convergence failure. The study suggests that MCMLE and MPLE should be the default approach to estimate ERGMs for small-variance and large-variance valued networks, respectively. We also offer further suggestions regarding choice of computational method for valued ERGMs based on data structure, available computational resources and analytical goals.