Reversible jump Markov chain Monte Carlo algorithms for Bayesian variable selection in logistic mixed models

In this article, to reduce computational load in performing Bayesian variable selection, we used a variant of reversible jump Markov chain Monte Carlo methods, and the Holmes and Held (HH) algorithm, to sample model index variables in logistic mixed models involving a large number of explanatory variables. Furthermore, we proposed a simple proposal distribution for model index variables, and used a simulation study and real example to compare the performance of the HH algorithm with our proposed and existing proposal distributions. The results show that the HH algorithm with our proposed proposal distribution is a computationally efficient and reliable selection method.     

Development of predictive signatures for treatment selection in precision medicine with survival outcomes

For survival endpoints in subgroup selection, a score conversion model is often used to convert the set of biomarkers for each patient into a univariate score and using the median of the univariate scores to divide the patients into biomarker‐positive and biomarker‐negative subgroups. However, this may lead to bias in patient subgroup identification regarding the 2 issues: (1) treatment is equally effective for all patients and/or there is no subgroup difference; (2) the median value of the univariate scores as a cutoff may be inappropriate if the sizes of the 2 subgroups are differ substantially. We utilize a univariate composite score method to convert the set of patient's candidate biomarkers to a univariate response score. We propose applying the likelihood ratio test (LRT) to assess homogeneity of the sampled patients to address the first issue. In the context of identification of the subgroup of responders in adaptive design to demonstrate improvement of treatment efficacy (adaptive power), we suggest that subgroup selection is carried out if the LRT is significant. For the second issue, we utilize a likelihood‐based change‐point algorithm to find an optimal cutoff. Our simulation study shows that type I error generally is controlled, while the overall adaptive power to detect treatment effects sacrifices approximately 4.5% for the simulation designs considered by performing the LRT; furthermore, the change‐point algorithm outperforms the median cutoff considerably when the subgroup sizes differ substantially.     

民族地区城镇化发展模式及路径创新——基于四川、广西、贵州等地的调研分析

城镇化是我国民族地区经济社会发展的必经阶段。探索民族地区的城镇化发展模式及创新路径，对于加速民族地区现代化进程、转变民族地区经济增长方式以及实现社会安定团结具有重要的推动作用。文章在对民族地区城镇化发展现状描述的基础之上，归纳概括出城镇化运行的四大矛盾；并全面总结出旅游产业带动型、工业发展助推型、边境贸易推动型、草原牧业发展型四大类型的城镇化发展模式；通过构建民族地区城镇化发展的愿景框架，结合民族地区的发展实际，最终提出坚持绿色低碳，发展城镇化的生态文明建设，强化产城融合，持续释放城镇化的发展活力，深化制度改革，实现城镇化发展质量的提升及保持文化传承，不断彰显城镇化的民族特色等城镇化发展的创新路径。     

基于修正UTAUT模型的农信社手机银行使用行为研究——以福建省农户调查数据为例

基于UTAUT模型,引入感知风险、替代软件等2个变量进行修正,构建农信社手机银行使用行为影响因素研究模型,并运用结构方程模型、Bootstrap中介检验、多群组分析等方法对福建省农户调查数据进行实证分析。研究发现:(1)感知易用性显著正向影响农信社手机银行使用意愿;感知风险和替代软件显著负向影响农信社手机银行使用意愿;社会影响对农信社手机银行使用意愿影响不显著。(2)便利条件和使用意愿显著正向影响农信社手机银行使用行为;农信社手机银行使用行为影响因素由大到小依次为便利条件、替代软件、感知风险、感知易用性。(3)教育在感知易用性对农信社手机银行使用意愿影响中具有调节作用。据此提出,应进一步增强操作的便利性以优化业务办理效率,增强信息的安全性以降低软件使用风险,增强软件的特色性以降低软件可替代性,增强软件的易用性以优化便捷使用体验,从而优化农村金融服务供给,助推农村经济发展。     

Does biomass energy consumption reduce total energy CO2 emissions in the US?

This study investigates the causal relationship between total biomass energy consumption, total energy CO₂ emissions, and GDP in the United States for the period January 1973–December 2016 by employing a directed acyclic graph (DAG) techniques and the Autoregressive Distributed Lag (ARDL) model. Additionally, this paper examines the existence of the environmental Kuznets curve (EKC) hypothesis. The result of the DAG reveals that total biomass energy consumption and GDP have a unidirectional contemporaneous causal relationship with total energy CO₂ emissions. Based on the results of the ARDL, we find that a 1% increase in per capita total biomass energy consumption causes a 0.65% reduction in per capita total energy CO₂ emissions in the long-run. This finding implies that expanding the usage of biomass is one way to reduce and control greenhouse gases in the US. Moreover, we find that the inverted U-shaped EKC hypothesis is satisfied for the US case. Findings from this study suggest that energy policies should stimulate an increase of biomass production for reducing total energy CO₂.     

A discrete-time risk model with Poisson ARCH claim-number process

Abstract

In this paper, we propose a discrete-time risk model with the claim number following an integer-valued autoregressive conditional heteroscedasticity (ARCH) process with Poisson deviates. In this model, the current claim number depends on the previous observations. Within this framework, the equation for finding the adjustment coefficient is derived. Numerical studies are also carried out to examine the impact of the Poisson ARCH dependence structure on the ruin probability.     

西方残障身份发展理论述评及本土化思考

积极的残障身份认同对残障者本身具有重要意义,残障身份发展是残障者实现身份认同的路径,理解残障身份发展的过程也可以让残疾人工作者更好地为残障者提供教育、康复等相关服务。本文通过梳理较为典型的西方残障身份发展理论,结合国内残障身份相关研究及本土社会文化背景展开讨论。文章认为,在西方残障身份发展理论框架下,残障者形成积极身份认同的关键在于认同并融入一套在残障社区内共享的残障文化,而在我国社会文化背景下,目前并没有一套成体系的残障文化,残障者也难以体会残障的文化身份感,国内残障者的身份认同缺少本土残障文化依靠。     

基于真实项目的本土化外事口译人才培养模式探究——以汤显祖戏剧节暨国际戏剧交流月活动为例

全球化进程加快,欠发达地区日益增长的对外交流的需求与本地高素质口译人才短缺的矛盾日益突出。围绕“本土化外事口译人才的培养”这一议题,以功能主义目的论和建构主义理论为指导,充分结合地方实际,以真实的口译项目为依托,通过实践探索,构建出一套“三阶段式”本土化外事口译人才培养模式,以期为欠发达地区本土化外事口译人才培养问题提供借鉴。     

基于神经网络的测井评价油层污染研究

利用测井资料,在剖析传统的测井评价油(气)层污染方法的基础上,介绍了利用BP神经网络这一现代数理统计新技术来实现测井多参数评定油气层污染程度的方法和技巧;以华北二连油田实际资料为例,建立了测井判释油气层污染的数学物理模型,并进行了模拟预测。结果表明:该法简便可行,对污染评价参数预测精度较高,对污染程度评价合理准确,可作为油(气)层污