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.     

Embodied versus Disembodied Information: How Online Artifacts Influence Offline Interpersonal Interactions

This article examines how the sheer volume of personal information recorded and searchable online (online artifacts) has transformed the situated activity system central to Goffman's dramaturgical theories. In‐depth interviews reveal that individuals believe disembodied information based on online artifacts is a more accurate representation of others than embodied information from spatially and temporally bounded face‐to‐face (FTF) processes because they represent how others have behaved over time and are attested by their online contacts. However, the n‐adic structure of online interaction leads to mismatched expectations about whether disembodied information is taken into account during FTF encounters, and consequently can result in embarrassment.     

Too Scared or Too Capable? Why Do College Students Stay Away from the H1N1 Vaccine?

Although college students were among the populations that had the highest frequency of infection for H1N1 influenza, only 8% of them received H1N1 vaccine this past flu season nationwide. During the peak of this pandemic, information about H1N1 vaccine was widely available. However, knowledge test and behavioral data indicated that most college students were not equipped with basic facts about H1N1 and the H1N1 vaccine. To investigate socio‐psychological factors that might have deterred this high‐risk population from learning about and getting the H1N1 vaccine, this study tested the utility of a risk information seeking model in addressing this health communication problem. Data collected from an online survey of 371 college students showed that respondents seemed to overestimate how much they knew about the vaccine. Risk information seeking, however, positively influenced their intentions to get the vaccine. Results suggested that to communicate effectively to this population, it is important to emphasize the difference between perceived knowledge and actual knowledge, monitor emotional responses to potential risks, and promote getting flu vaccination as a socially desirable behavior.     

学前教育师范生TPACK现状调查及教学改革建议

在“互联网+”及多元文化知识不断融合的教育背景下，信息技术在教学中被提升到一个新的高度。为了解学前教育师范生群体对信息技术知识在教学活动中的应用程度，笔者采用自编的《学前教育师范生TPACK》量表对220名在校学前教育师范生进行调查。统计分析发现：1）学前教育师范生的总体TPACK知识掌握较好，但技术知识仍然较为薄弱；2）学前教育师范生TPACK知识存在显著的年级差异，但性别差异不显著；3）信息技术爱好和训练会造成学前教育师范生TPACK知识的差异。应采取的对策：1）注重培养学前教育师范生的信息技术知识和能力；2）大力推进第二课堂，促进TPK和TCK的发展；3）进行产学研合作，提升学前教育师范生的TPACK水平。     

Empirical Likelihood Inference for the Rao‐Hartley‐Cochran Sampling Design

The Hartley‐Rao‐Cochran sampling design is an unequal probability sampling design which can be used to select samples from finite populations. We propose to adjust the empirical likelihood approach for the Hartley‐Rao‐Cochran sampling design. The approach proposed intrinsically incorporates sampling weights, auxiliary information and allows for large sampling fractions. It can be used to construct confidence intervals. In a simulation study, we show that the coverage may be better for the empirical likelihood confidence interval than for standard confidence intervals based on variance estimates. The approach proposed is simple to implement and less computer intensive than bootstrap. The confidence interval proposed does not rely on re‐sampling, linearization, variance estimation, design‐effects or joint inclusion probabilities.     

基于支持向量机的管理信息系统故障识别模型研究

管理信息系统故障影响着企业信息工程的开展。及时识别故障,能够为企业争取更多时间处理故障。因此,管理信息系统故障识别有着重要的研究意义和实践意义。基于支持向量机,构建管理信息系统故障识别模型,通过把样本应用于该模型,结果表明该模型具有较好的准确性,能有效识别管理信息系统故障。     

Some properties of Lin–Wong divergence on the past lifetime data

Measures of statistical divergence are used to assess mutual similarities between distributions of multiple variables through a variety of methodologies including Shannon entropy and Csiszar divergence. Modified measures of statistical divergence are introduced throughout the present article. Those modified measures are related to the Lin–Wong (LW) divergence applied on the past lifetime data. Accordingly, the relationship between Fisher information and the LW divergence measure was explored when applied on the past lifetime data. Throughout this study, a number of relations are proposed between various assessment methods which implement the Jensen–Shannon, Jeffreys, and Hellinger divergence measures. Also, relations between the LW measure and the Kullback–Leibler (KL) measures for past lifetime data were examined. Furthermore, the present study discusses the relationship between the proposed ordering scheme and the distance interval between LW and KL measures under certain conditions.     

Conditional covariance penalties for mixed models

The prediction error for mixed models can have a conditional or a marginal perspective depending on the research focus. We introduce a novel conditional version of the optimism theorem for mixed models linking the conditional prediction error to covariance penalties for mixed models. Different possibilities for estimating these conditional covariance penalties are introduced. These are bootstrap methods, cross-validation, and a direct approach called Steinian. The behavior of the different estimation techniques is assessed in a simulation study for the binomial-, the t-, and the gamma distribution and for different kinds of prediction error. Furthermore, the impact of the estimation techniques on the prediction error is discussed based on an application to undernutrition in Zambia.     

An algorithmic approach to construct D-optimal saturated designs for logistic model

In this paper, locally D-optimal saturated designs for a logistic model with one and two continuous input variables have been constructed by modifying the famous Fedorov exchange algorithm. A saturated design not only ensures the minimum number of runs in the design but also simplifies the row exchange computation. The basic idea is to exchange a design point with a point from the design space. The algorithm performs the best row exchange between design points and points form a candidate set representing the design space. Naturally, the resultant designs depend on the candidate set. For gain in precision, intuitively a candidate set with a larger number of points and the low discrepancy is desirable, but it increases the computational cost. Apart from the modification in row exchange computation, we propose implementing the algorithm in two stages. Initially, construct a design with a candidate set of affordable size and then later generate a new candidate set around the points of design searched in the former stage. In order to validate the optimality of constructed designs, we have used the general equivalence theorem. Algorithms for the construction of optimal designs have been implemented by developing suitable codes in R.     

Major Accidents (Gray Swans) Likelihood Modeling Using Accident Precursors and Approximate Reasoning

Compared to the remarkable progress in risk analysis of normal accidents, the risk analysis of major accidents has not been so well‐established, partly due to the complexity of such accidents and partly due to low probabilities involved. The issue of low probabilities normally arises from the scarcity of major accidents’ relevant data since such accidents are few and far between. In this work, knowing that major accidents are frequently preceded by accident precursors, a novel precursor‐based methodology has been developed for likelihood modeling of major accidents in critical infrastructures based on a unique combination of accident precursor data, information theory, and approximate reasoning. For this purpose, we have introduced an innovative application of information analysis to identify the most informative near accident of a major accident. The observed data of the near accident were then used to establish predictive scenarios to foresee the occurrence of the major accident. We verified the methodology using offshore blowouts in the Gulf of Mexico, and then demonstrated its application to dam breaches in the United Sates.