Graphical Techniques for Selecting Explanatory Variables for Time Series Data

Bayesian model building techniques are developed for data with a strong time series structure and possibly exogenous explanatory variables that have strong explanatory and predictive power. The emphasis is on finding whether there are any explanatory variables that might be used for modelling if the data have a strong time series structure that should also be included. We use a time series model that is linear in past observations and that can capture both stochastic and deterministic trend, seasonality and serial correlation. We propose the plotting of absolute predictive error against predictive standard deviation. A series of such plots is utilized to determine which of several nested and non-nested models is optimal in terms of minimizing the dispersion of the predictive distribution and restricting predictive outliers. We apply the techniques to modelling monthly counts of fatal road crashes in Australia where economic, consumption and weather variables are available and we find that three such variables should be included in addition to the time series filter. The approach leads to graphical techniques to determine strengths of relationships between the dependent variable and covariates and to detect model inadequacy as well as determining useful numerical summaries.     

Root-n-consistent semiparametric estimation of partially linear models based on k-nn method

In the context of the partially linear semiparametric model examined by Robinson (1988), we show that root-n-consisten estimation results established using kernel and series methods can also be obtained by using k-nearest-neighbor (k-nn) method.     

地理距离、方言文化与劳动力空间流动

本文基于百度迁徙大数据研究中国劳动力的空间流动，系统考察地理和文化对劳动力流动的影响。通过构造流动机会比率，基于引力模型和普通最小二乘法的研究表明地理距离和方言距离阻碍劳动力流动。在空间距离上，劳动力偏好邻近城市，地理距离每增加1%，劳动力的流动机会比率降低约0.6%。在空间位置上，劳动力倾向于在方言文化相近地域范围流动，方言距离每增加1%，劳动力的流动机会比率下降2%左右。通过构造两地年均降水量差距和小麦种植适宜度差距作为方言距离的工具变量，以两阶段最小二乘法估计缓解内生性问题，估计显示结论稳健。考虑普通话因素后方言距离的抑制影响依然稳健，但目的地的高普通话普及率显著发挥促进劳动力流动的引力作用。最后，本文得出持续推广普通话、加强交通建设和深化中等教育的政策建议。     

递阶双因素模型估计信用组合一致性风险

当信用组合内资产个数较少时，基于Gordy提出的粒度调节方法计算的组合风险量度VaR将严重被低估，从而高估一致性风险量度预期短缺ES。本文利用Taylor展开式改进粒度调节方法，提高估计ES的准确性；并且假设行业因素是宏观经济因素的线性函数，由此保证了组合不变性的条件，从而扩展单因素模型为递阶双因素模型，提高相关性估计准确性，解决了单因素模型高估经济资本的问题。模拟结果显示递阶双因素模型的优越性，特别是组合内资产规模较小时，改进的效果更明显。     

口译笔记的认知理据

口译笔记是影响口译质量的一个重要因素。文章从认知科学的角度出发分析了口译笔记的特点和规律,指出口译笔记的具体操作和激活扩散模型、图式理论、连通主义等许多认知领域的研究成果有密切的联系,其中尤其是吉尔的精力分配理论,对如何做口译笔记有很强的实践指导作用。     

Social Relations Model Analyses of Perceived Self‐Control and Trust in Families

How do people know which family member is trustworthy? In this study, the authors tested the hypothesis that people use their perception of a family member's self‐control as an indicator of his or her trustworthiness. Eighty‐four Dutch families consisting of 2 parents and 2 children completed questionnaires assessing each family member's trust in and perceived self‐control of the other 3 family members. This full‐family design enabled the authors to examine their hypothesis in horizontal relationships, between family members of equal status (i.e., parent–parent and sibling–sibling relationships), and vertical relationships, in which partners have unequal status (i.e., parent–child and child–parent relationships). Consistent with the hypothesis, Social Relations Model analyses showed that being perceived as having higher self‐control is related to greater trustworthiness among adults and children in the large majority of horizontal and vertical relationships (10 out of 12). These findings highlight that perceived self‐control is an important factor by which to gauge trustworthiness in families.     

Model Selection Information Criteria in Latent Class Models with Missing Data and Contingency Question

Latent class analysis (LCA) has been found to have important applications in social and behavioral sciences for modeling categorical response variables, and nonresponse is typical when collecting data. In this study, the nonresponse mainly included “contingency questions” and real “missing data.” The primary objective of this research was to evaluate the effects of some potential factors on model selection indices in LCA with nonresponse data.

We simulated missing data with contingency questions and evaluated the accuracy rates of eight information criteria for selecting the correct models. The results showed that the main factors are latent class proportions, conditional probabilities, sample size, the number of items, the missing data rate, and the contingency data rate. Interactions of the conditional probabilities with class proportions, sample size, and the number of items are also significant. From our simulation results, the impact of missing data and contingency questions can be amended by increasing the sample size or the number of items.     

General Sparse Boosting: Improving Feature Selection of L2 Boosting by Correlation-Based Penalty Family

In high-dimensional setting, componentwise L₂boosting has been used to construct sparse model that performs well, but it tends to select many ineffective variables. Several sparse boosting methods, such as, SparseL₂Boosting and Twin Boosting, have been proposed to improve the variable selection of L₂boosting algorithm. In this article, we propose a new general sparse boosting method (GSBoosting). The relations are established between GSBoosting and other well known regularized variable selection methods in the orthogonal linear model, such as adaptive Lasso, hard thresholds, etc. Simulation results show that GSBoosting has good performance in both prediction and variable selection.     

论应用伦理学的判例模式

应用伦理学的"判例模式"可以取代日益受到质疑的"工程模式"。"判例模式"的运用是在伦理委员会这个"道德实验室"里进行的。它指通过一定的程序,在先前的与现实的道德事件的比较权衡中来解决道德困境,作出道德决策的过程。应用伦理学的"判例模式"的确立,有助于通过一种循序渐进的方法,实现道德与社会需要的动态统一与均衡、创造出新的道德结构。     

Testing for Serial Independence: Beyond the Portmanteau Approach

Portmanteau tests are typically used to test serial independence even if, by construction, they are generally powerful only in presence of pairwise dependence between lagged variables. In this article, we present a simple statistic defining a new serial independence test, which is able to detect more general forms of dependence. In particular, differently from the Portmanteau tests, the resulting test is powerful also under a dependent process characterized by pairwise independence. A diagram, based on p-values from the proposed test, is introduced to investigate serial dependence. Finally, the effectiveness of the proposal is evaluated in a simulation study and with an application on financial data. Both show that the new test, used in synergy with the existing ones, helps in the identification of the true data-generating process. Supplementary materials for this article are available online.