大数据与政府治理能力提升:机遇、挑战与展望

随着社会问题复杂程度的不断增加,大数据应用为政府解决棘手问题、提升治理能力、应对挑战提供了新思路,但也带来了一定程度的适应难题。因此,探析大数据在我国的政府治理实践中如何得到合理应用、有效推动治理能力提升需要结合国内外成功经验,从机遇和挑战两个维度进行理解。分析发现,大数据可以推动政府治理协同化、政府决策科学化、数据管理规范化,并会遇到数据壁垒、专业能力欠缺以及政策法规滞后等方面的阻碍。研究意义在于分析大数据对政府治理能力提升的作用,为未来我国政府在治理实践中更好地利用大数据技术提供参考。     

Simultaneous estimation and inference for multiple response variables

Multivariate data arise frequently in biomedical and health studies where multiple response variables are collected across subjects. Unlike a univariate procedure fitting each response separately, a multivariate regression model provides a unique opportunity in studying the joint evolution of various response variables. In this paper, we propose two estimation procedures that improve estimation efficiency for the regression parameter by accommodating correlations among the response variables. The proposed procedures do not require knowledge of the true correlation structure nor does it estimate the parameters associated with the correlation. Theoretical and simulation results confirm that the proposed estimators are more efficient than the one obtained from the univariate approach. We further propose simple and powerful inference procedures for a goodness-of-fit test that possess the chi-squared asymptotic properties. Extensive simulation studies suggest that the proposed tests are more powerful than the Wald test based on the univariate procedure. The proposed methods are also illustrated through the mother’s stress and children’s morbidity study.     

Bayesian spatial quantile regression for areal count data,with application on substitute care placements in Texas

Quantile regression (QR) allows one to model the effect of covariates across the entire response distribution, rather than only at the mean, but QR methods have been almost exclusively applied to continuous response variables and without considering spatial effects. Of the few studies that have performed QR on count data, none have included random spatial effects, which is an integral facet of the Bayesian spatial QR model for areal counts that we propose. Additionally, we introduce a simplifying alternative to the response variable transformation currently employed in the QR for counts literature. The efficacy of the proposed model is demonstrated via simulation study and on a real data application from the Texas Department of Family and Protective Services (TDFPS). Our model outperforms a comparable non-spatial model in both instances, as evidenced by the deviance information criterion (DIC) and coverage probabilities. With the TDFPS data, we identify one of four covariates, along with the intercept, as having a nonconstant effect across the response distribution.     

Bibliometrics of Big Data Research Literature in Social Science

[Purpose/Significance] This article provides a bibliometric study of the big data research literature in social science to explore its features and patterns both in domestic and international area, quantitatively and qualitatively. [Method/Process] This article collects the data from academic databases, then makes statistical analysis on significant characteristics to evaluate the regularity of document increase, distribution of subject categories, most prolific and impactful institutions, and then discusses the relations between research front and intellectual base via citation and co-citation analysis, finally concludes research features and patterns of big data. [Result/Conclusion] (1) Different subject categories show the different level of research interest while big data research has been one of the most popular topics in social science. The most documents are from information science and library science. (2) Besides journals of high output subject categories, computer science, telecommunications and multidisciplinary journals are common intellectual bases of big data research in social science. (3) Renmin University of China in domestic and Harvard University in the world are top institutions according to the number of fruits and influence of their publications of big data research in social science. (4) For China, publication quantity in social science makes apparent growth while international academic influence is still limited.     

块预处理正交极小化方法及其在水平井油藏模拟中的应用

本文介绍了4种块不完全分解ORTHOMIN加速方法,其中自然网格排序上有三种,即一、二、三级块不完全分解和D4网格排序上的一级块不完全分解,并将此方法用于水平井的油藏模拟,取得了满意的效果。     

Modelling hierarchical clustered censored data with the hierarchical Kendall copula

This article proposes a new model for right‐censored survival data with multi‐level clustering based on the hierarchical Kendall copula model of Brechmann (2014) with Archimedean clusters. This model accommodates clusters of unequal size and multiple clustering levels, without imposing any structural conditions on the parameters or on the copulas used at various levels of the hierarchy. A step‐wise estimation procedure is proposed and shown to yield consistent and asymptotically Gaussian estimates under mild regularity conditions. The model fitting is based on multiple imputation, given that the censoring rate increases with the level of the hierarchy. To check the model assumption of Archimedean dependence, a goodness‐of test is developed. The finite‐sample performance of the proposed estimators and of the goodness‐of‐fit test is investigated through simulations. The new model is applied to data from the study of chronic granulomatous disease. The Canadian Journal of Statistics 47: 182–203; 2019 © 2019 Statistical Society of Canada     

Synergy evaluation of non-normalizable dose–response data: Generalization of combination index for the linear effect of drugs

The study of drug synergy plays a prominent role in the search for drug combinations with beneficial interactions. Firstly, in this process, the drug-effect response of individual parts and the mixture needs to be derived. This function is usually well described by Hill (or other logistic or sigmoid) curve. Due to its boundedness, it allows the measured data to be normalized. The normalized data can then be processed by interaction analysis using the Loewe, Bliss, or other models to evaluate possible synergy or antagonism of two or more drugs. However, sometimes, the drug-effect responses observed in pharmaceutical research do not appear to be bounded. Theoretically, the drug-effect curve cannot grow to infinity, but it may be impossible to determine its upper bound within the observed region. In this case, standard models cannot be used, since they assume that data are normalized. The approach of this article bypasses the need to normalize the data, allowing its broader application and usefulness in finding potential synergies in pharmaceutical research.     

试述图表资料在生物教学中的作用

图表资料具有直观性、系统性及概括性的特点,在生物教学中有着重要地位及作用．