Influencing support of charitable objectives in the near and distant future: delay discounting and the moderating influence of construal level

Two studies investigated the delay discounting effect in charitable giving situations, and whether construal level moderates this effect. In both studies, construal level was manipulated by inducing a concrete vs. an abstract mindset. Participants were asked how much time (Experiment 1) or how much money (Experiment 2) they would be willing to volunteer or donate, respectively, to a self-chosen charitable organization in either the near or distant future. Participants committed themselves to contributing more time and money in the distant than in the near future. Additionally, an abstract (vs. concrete) mindset reduced this delay discounting effect, particularly for monetary donations. Delay discounting of costs associated with altruistic behavior and the role of construal level in increasing donations and volunteering are discussed.     

Unbiased Estimator for a Covariance Matrix Under Two-Step Monotone Incomplete Sample

In this article, we consider an inference for a covariance matrix under two-step monotone incomplete sample. The maximum likelihood estimator of the mean vector is unbiased but that of the covariance matrix is biased. We derive an unbiased estimator for the covariance matrix using some fundamental properties of the Wishart matrix. The properties of the estimators are investigated and the accuracies are checked by a numerical simulation.     

我国教育技术学硕士学位论文的可视化研究——基于社会网络分析视角

关键词是文献的重要组成部分,凝聚了文献的论述核心。选取教育技术学专业硕士学位论文的关键词,经过数据处理形成共词矩阵。采用社会网络分析（SNA）方法和工具,从绘制网络社群图、中心性分析、网络密度分析、小世界效应分析、核心—边缘分析等角度做了详细的论述,指出了我国教育技术学研究生的研究方向及现状。     

Discriminating among Weibull,log-normal,and log-logistic distributions

In this article, we consider the problem of the model selection/discrimination among three different positively skewed lifetime distributions. All these three distributions, namely; the Weibull, log-normal, and log-logistic, have been used quite effectively to analyze positively skewed lifetime data. In this article, we have used three different methods to discriminate among these three distributions. We have used the maximized likelihood method to choose the correct model and computed the asymptotic probability of correct selection. We have further obtained the Fisher information matrices of these three different distributions and compare them for complete and censored observations. These measures can be used to discriminate among these three distributions. We have also proposed to use the Kolmogorov–Smirnov distance to choose the correct model. Extensive simulations have been performed to compare the performances of the three different methods. It is observed that each method performs better than the other two for some distributions and for certain range of parameters. Further, the loss of information due to censoring are compared for these three distributions. The analysis of a real dataset has been performed for illustrative purposes.     

Counting by weighing: construction of two-sided confidence intervals

Counting by weighing is widely used in industry and often more efficient than counting manually which is time consuming and prone to human errors especially when the number of items is large. Lower confidence bounds on the numbers of items in infinitely many future bags based on the weights of the bags have been proposed recently in Liu et al. [Counting by weighing: Know your numbers with confidence, J. Roy. Statist. Soc. Ser. C 65(4) (2016), pp. 641–648]. These confidence bounds are constructed using the data from one calibration experiment and for different parameters (or numbers), but have the frequency interpretation similar to a usual confidence set for one parameter only. In this paper, the more challenging problem of constructing two-sided confidence intervals is studied. A simulation-based method for computing the critical constant is proposed. This method is proven to give the required critical constant when the number of simulations goes to infinity, and shown to be easily implemented on an ordinary computer to compute the critical constant accurately and quickly. The methodology is illustrated with a real data example.     

Setting Priorities in Behavioral Interventions: An Application to Reducing Phishing Risk

Phishing risk is a growing area of concern for corporations, governments, and individuals. Given the evidence that users vary widely in their vulnerability to phishing attacks, we demonstrate an approach for assessing the benefits and costs of interventions that target the most vulnerable users. Our approach uses Monte Carlo simulation to (1) identify which users were most vulnerable, in signal detection theory terms; (2) assess the proportion of system‐level risk attributable to the most vulnerable users; (3) estimate the monetary benefit and cost of behavioral interventions targeting different vulnerability levels; and (4) evaluate the sensitivity of these results to whether the attacks involve random or spear phishing. Using parameter estimates from previous research, we find that the most vulnerable users were less cautious and less able to distinguish between phishing and legitimate emails (positive response bias and low sensitivity, in signal detection theory terms). They also accounted for a large share of phishing risk for both random and spear phishing attacks. Under these conditions, our analysis estimates much greater net benefit for behavioral interventions that target these vulnerable users. Within the range of the model's assumptions, there was generally net benefit even for the least vulnerable users. However, the differences in the return on investment for interventions with users with different degrees of vulnerability indicate the importance of measuring that performance, and letting it guide interventions. This study suggests that interventions to reduce response bias, rather than to increase sensitivity, have greater net benefit.     

A Review of Risk Matrices Used in Acute Hospitals in England

In healthcare, patient safety has received substantial attention and, in turn, a number of approaches to managing safety have been adopted from other high‐risk industries. One of these has been risk assessment, predominantly through the use of risk matrices. However, while other industries have criticized the design and use of these risk matrices, the applicability of such criticism has not been investigated formally in healthcare. This study examines risk matrices as used in acute hospitals in England and the guidance provided for their use. It investigates the applicability of criticisms of risk matrices from outside healthcare through a document analysis of the risk assessment policies, procedures, and strategies used in English hospitals. The findings reveal that there is a large variety of risk matrices used, where the design of some might increase the chance of risk misprioritization. Additionally, findings show that hospitals may provide insufficient guidance on how to use risk matrices as well as what to do in response to the existing criticisms of risk matrices. Consequently, this is likely to lead to variation in the quality of risk assessment and in the subsequent deployment of resources to manage the assessed risk. Finally, the article outlines ways in which hospitals could use risk matrices more effectively.     

An improved meta-analysis for analyzing cylindrical-type time series data with applications to forecasting problem in environmental study

In this paper, we propose an improved generalized least square (GLS) meta-analysis in a linear-circular regression, and show its utility in the analysis of a certain environmental issue. The existing GLS meta-analysis proposed in Becker and Wu has a serious flaw since information about the covariance among coefficients across studies is not utilized. In our proposed meta-analysis, we take the correlations between adjacent studies into account, and improve the existing GLS meta-analysis. We provide numerical examples to compare the proposed method with several other existing methods by using Akaike's Information Criterion, Bayesian Information Criterion and mean square prediction errors with applications to forecasting problem in Environmental study.     

Asymptotics and bootstrap for random-effects panel data transformation models

This article investigates the asymptotic properties of quasi-maximum likelihood (QML) estimators for random-effects panel data transformation models where both the response and (some of) the covariates are subject to transformations for inducing normality, flexible functional form, homoskedasticity, and simple model structure. We develop a QML-type procedure for model estimation and inference. We prove the consistency and asymptotic normality of the QML estimators, and propose a simple bootstrap procedure that leads to a robust estimate of the variance-covariance (VC) matrix. Monte Carlo results reveal that the QML estimators perform well in finite samples, and that the gains by using the robust VC matrix estimate for inference can be enormous.     

Multi-relational learning via hierarchical nonparametric Bayesian collective matrix factorization

Relational learning addresses problems where the data come from multiple sources and are linked together through complex relational networks. Two important goals are pattern discovery (e.g. by (co)-clustering) and predicting unknown values of a relation, given a set of entities and observed relations among entities. In the presence of multiple relations, combining information from different but related relations can lead to better insights and improved prediction. For this purpose, we propose a nonparametric hierarchical Bayesian model that improves on existing collaborative factorization models and frames a large number of relational learning problems. The proposed model naturally incorporates (co)-clustering and prediction analysis in a single unified framework, and allows for the estimation of entire missing row or column vectors. We develop an efficient Gibbs algorithm and a hybrid Gibbs using Newton's method to enable fast computation in high dimensions. We demonstrate the value of our framework on simulated experiments and on two real-world problems: discovering kinship systems and predicting the authors of certain articles based on article–word co-occurrence features.