略论邓小平理论萌芽的界定及其标志

作为邓小平理论萌芽的标志不是某一具体的时间、论著、观点。对邓小平理论萌芽应作出系统的、全面的科学界定。邓小平理论萌芽的标志应该是时间段、论著链、观点网     

网络文学的当下可能性

本文根据当前对网络文学的定义 ,从三个方面分析说明了网络文学出现的当下可能性。一是网络媒介的出现引起文学载体和传播方式的变化 ,形成了自己的文学群落 ,培养了一大批文学爱好者的文学受众。二是作为写作的网络文学 ,实际上是对当前出现的知识霸权和文学生产麦当劳化的反拨 ,使文学向着自由无功利和审美愉悦性传统回归。三是网络文学作为一种题材 ,与传统文学无本质上的区别 ,是网络出现后形成的一种特定群体生活状态的反映 ,从而在题材选取 ,主题表现及表现技巧上具有其独特性。     

物质世界的一次精神突围--解读网络小说《可以说谎可以爱》

在物欲世界的重压之下，正常的人本能地寻求放松压抑的心灵、修复扭曲的人性的方式，艾闪的网络小说《可以说谎可以爱》表现的就是对被压抑的人生的逃避，试图在逃避中寻找现代人的精神出路，重新确立人生价值。     

双向FDI与经济增长的实证研究

从双向FDI的角度分析OFDI和IFDI与经济增长的关系。选取双向FDI对经济增长的3个主要影响因素,分别对双向FDI与GDP、投资、净出口和产业结构的关系进行实证分析。研究发现:双向FDI对于GDP和投资都具有拉动作用;IFDI对投资的影响滞后4期才能产生,对净出口的增长起到正向促进作用;OFDI的扩大不利于净出口的增长;双向FDI与三次产业都存在较强的灰色关联度。据此,提出改善对内对外投资环境、发挥双向FDI对固定资产投资和净出口的促进作用、强化双向FDI对产业结构的调整作用等建议。     

Robust sparse regression and tuning parameter selection via the efficient bootstrap information criteria

There is currently much discussion about lasso-type regularized regression which is a useful tool for simultaneous estimation and variable selection. Although the lasso-type regularization has several advantages in regression modelling, owing to its sparsity, it suffers from outliers because of using penalized least-squares methods. To overcome this issue, we propose a robust lasso-type estimation procedure that uses the robust criteria as the loss function, imposing L₁-type penalty called the elastic net. We also introduce to use the efficient bootstrap information criteria for choosing optimal regularization parameters and a constant in outlier detection. Simulation studies and real data analysis are given to examine the efficiency of the proposed robust sparse regression modelling. We observe that our modelling strategy performs well in the presence of outliers.     

Navigating health care: brokerage and access for undocumented Latino immigrants under the 2010 Affordable Care Act

ABSTRACT

Brokerage is a prominent mechanism that explains access for Latino immigrants to many American institutions. However, few studies examine the dual nature of brokerage in answering questions on access to health care for undocumented Latino immigrants. The growing importance of Federally Qualified Health Centers (FQHCs) and ethnic navigators connecting immigrants to health care under the 2010 Affordable Care Act calls attention to the duality of brokers and its role in access. The study draws from 44 in-depth interviews with providers, clinic directors, navigators, and immigrant patients and two years of fieldwork in FQHCs in California’s San Francisco Bay Area (2011–2013). Results show that brokers enable access through coaching and myth-busting but they also hinder it through scrutiny and bureaucratic filtering. The widespread dependence on brokerage, I argue, leads to ersatz brokerage, such as when providers navigate the health-care system for the undocumented without the appropriate linguistic resources or coordination. Furthermore, the study shows that the duality in brokerage generates misinformation, churning, and alienation. Understanding the relational processes that both include and exclude vulnerable populations from needed services enhances theories of immigrant integration and can help design more efficient policies and address inequalities related to documentation status.     

Bayesian elastic net single index quantile regression

Single index model conditional quantile regression is proposed in order to overcome the dimensionality problem in nonparametric quantile regression. In the proposed method, the Bayesian elastic net is suggested for single index quantile regression for estimation and variables selection. The Gaussian process prior is considered for unknown link function and a Gibbs sampler algorithm is adopted for posterior inference. The results of the simulation studies and numerical example indicate that our propose method, BENSIQReg, offers substantial improvements over two existing methods, SIQReg and BSIQReg. The BENSIQReg has consistently show a good convergent property, has the least value of median of mean absolute deviations and smallest standard deviations, compared to the other two methods.     

Bayesian robustness of the compound Poisson distribution under bidimensional prior: an application to the collective risk model

The distribution of the aggregate claims in one year plays an important role in Actuarial Statistics for computing, for example, insurance premiums when both the number and size of the claims must be implemented into the model. When the number of claims follows a Poisson distribution the aggregated distribution is called the compound Poisson distribution. In this article we assume that the claim size follows an exponential distribution and later we make an extensive study of this model by assuming a bidimensional prior distribution for the parameters of the Poisson and exponential distribution with marginal gamma. This study carries us to obtain expressions for net premiums, marginal and posterior distributions in terms of some well-known special functions used in statistics. Later, a Bayesian robustness study of this model is made. Bayesian robustness on bidimensional models was deeply treated in the 1990s, producing numerous results, but few applications dealing with this problem can be found in the literature.     

Oracle Inequalities for Convex Loss Functions with Nonlinear Targets

This article considers penalized empirical loss minimization of convex loss functions with unknown target functions. Using the elastic net penalty, of which the Least Absolute Shrinkage and Selection Operator (Lasso) is a special case, we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target is linear, this inequality also provides an upper bound of the estimation error of the estimated parameter vector. Next, we use the non-asymptotic results to show that the excess loss of our estimator is asymptotically of the same order as that of the oracle. If the target is linear, we give sufficient conditions for consistency of the estimated parameter vector. We briefly discuss how a thresholded version of our estimator can be used to perform consistent variable selection. We give two examples of loss functions covered by our framework.     

论图书馆的信息推送服务

信息推送服务是一种个性化的主动信息服务，图书馆在服务过程中必须贯彻以人为本、主动服务、一站式服务、个性化服务理念；网络环境下，基于RSS的信息推送模式比较好的解决信息推送的技术手段，图书馆根据学校的特性选择信息，建立用户需求库、信息资源库，定制用户界面并协同数据库完成信息推送。因此，以用户为中心的信息推送技术必将成为未来信息获取技术的一个重要发展方向。