Direct estimation of differential networks under high-dimensional nonparanormal graphical models

In genomics, it is often of interest to study the structural change of a genetic network between two phenotypes. Under Gaussian graphical models, the problem can be transformed to estimating the difference between two precision matrices, and several approaches have been recently developed for this task such as joint graphical lasso and fused graphical lasso. However, the multivariate Gaussian assumptions made in the existing approaches are often violated in reality. For instance, most RNA-Seq data follow non-Gaussian distributions even after log-transformation or other variance-stabilizing transformations. In this work, we consider the problem of directly estimating differential networks under a flexible semiparametric model, namely the nonparanormal graphical model, where the random variables are assumed to follow a multivariate Gaussian distribution after a set of monotonically increasing transformations. We propose to use a novel rank-based estimator to directly estimate the differential network, together with a parametric simplex algorithm for fast implementation. Theoretical properties of the new estimator are established under a high-dimensional setting where $p$ grows with $n$ almost exponentially fast. In particular, we show that the proposed estimator is consistent in both parameter estimation and support recovery. Both synthetic data and real genomic data are used to illustrate the promise of the new approach. The Canadian Journal of Statistics 48: 187–203; 2020 © 2019 Statistical Society of Canada     

A Study of the Dynamic Mechanisms and Effects of Allocating Emissions Permits under Endogenous Growth

The allocation of carbon emissions permits is a major component of the design of carbon trading schemes. The choice of allocation mechanisms can influence the long-term growth path, which in turn affects the effectiveness of the emissions reduction policy. The findings of this article indicate that while a static allocation mechanism can maximize current yields, it leads to a slowdown in output growth under endogenous economic growth. To optimize intertemporal economic output, the allocation of carbon emissions will need to be dynamically adjusted. In the absence of complete information, an output-oriented adjustment to the allocation of emissions rights in different periods can guide market trading so that it approaches a long-term optimal growth path.     

“双核三方”模式下的高职旅游管理专业“2+1”人才培养模式——以广州城市职业学院为例

“双核三方”模式是一种创新型的人才培养模式.此模式以订单培养为切入点,以校企合作为支撑,以社区服务为宗旨,以旅游、会展服务与营销技能为核心,以岗位典型工作任务为载体,强化旅游服务与营销技能培养,推进人才培养模式的改革,培养满足区域旅游、会展行业的人才需求.文章以广州城市职业学院为例,尝试为高职院校专业人才培养提供参考和...     

“甲型H1N1流感”之语言生成

语言静态是相对的,动态是绝对的。当下“猪流感”到“甲型H1N1流感”术语变更的实际情形表明,语言生成会经过“基础、修正、生成”的直线过程,经受从言语到语言的心理认知过程。     

二语写作过程中母语使用的量化分析

采用发声思维、回顾性访谈、问卷调查等方法记录了16名英语专业学生在二语写作过程中的思维活动,并对该过程中的母语使用量及其与思维活动、二语水平间的相关性进行深入探讨.结果表明:1)母语被广泛地应用于二语写作过程,且平均的母语使用量占整个思维总量的39.60%;2)母语使用量随着二语水平的不继提高而减少;3)语言使用中的母语使用量与总体评分呈负相关.在以上研究结果的基础上,改建了二语写作模型,为二语写作的教与学提供一些参考.     

脉冲激光沉积法制备Zn0薄膜中衬底温度对Zn0薄膜结构的影响

采用溶胶——凝胶法制备高纯的靶材,利用PLD法生长薄膜,成功地在Si衬底上生长了高质量的ZnO薄膜,系统的研究了衬底温度对薄膜生长的影响。采用XRD对薄膜结构进行分析。XRD测试表明,大部分薄膜都具有高度的C轴择优生长取向性,只有衬底温度过高薄膜结构才发生改变。     

基于甲型H1N1流感疫情的应急物流体系流程研究

应急物流的高效运作能够降低突发事件造成的危害,因而得到了理论界的广泛关注。鉴于甲流感疫情下我国防控工作的严峻性,在指出我国现阶段应急物流中存在的问题并提出相关建议后,构建了疫情防控工作下的应急物流体系,并对每个流程做了详细阐述。     

高校师资队伍学缘结构评价和预测方法研究Ⅱ：案例分析

以福建某大学为研究案例,通过对该校近5个年师资队伍状况的调查,利用笔者提出的多样性指数模型和GM（1,1）模型对师资队伍学缘结构进行定量评价和变化趋势预测。结果表明,师资队伍多样性指数大小顺序为：2006--07学年（2．9043）〉2005—06学年（2．9031）〉2008—09学年（2．8800）〉2004—05学年（2．8495）〉2007—08学年（2．8352）;GM（1,1）模型预测显示该校2008--09学年以后师资队伍多样性指数呈下降趋势,学缘结构趋向于不良的方向。此外,文章还对学缘结构多样性指数降低的原因以及改善学缘结构的方法进行初步分析。     

On batch queueing systems: A combinatorial approach

We consider batch queueing systems M/M^H/1$M/M^H/1$ and M^H/M/1$M^H/M/1$ with catastrophes. The transient probability functions of these queueing systems are obtained by a Lattice Path Combinatorics approach that utilizes randomization and dual processes. Steady state distributions are also determined. Generalization to systems having batches of different sizes are discussed.     

无偏GM（1,1）幂模型及其应用

针对GM（1,1）幂模型灰微分方程与白化方程无法匹配的缺陷,以灰微分方程的重构为基础,建立无偏GM（1,1）幂模型。该方法使得差分方程的参数与其在微分方程中对应的参数具有更好的一致性。将无偏GM（1,1）幂模型应用到旅游客源预测中,实例应用结果显示无偏GM（1,1）幂模型预测精度高于GM（1,1）模型。