Assessing the Reliability of the Gambling Functional Assessment

Dixon and Johnson (Analysis of Gambling Behavior 2007, 1:44–49) introduced the Gambling Functional Assessment (GFA), which attempts to identify the consequences that may be maintaining a person’s gambling behavior. The present study had 949 introductory psychology students complete the GFA, with 124 of them completing the measure a second time 12 weeks later. Measures of internal consistency were quite good regardless of whether “non-gamblers” were included or excluded. Test–retest reliability was somewhat mixed, with Escape scores yielding substandard coefficients, especially among females. Both internal and test–retest reliability were typically poorer for female respondents. In general, the GFA performed within the limits of acceptable reliability, and coefficients compared favorably with similar measures. Future investigations will need to determine the reliability and validity of the instrument, especially as it pertains to its intended population, pathological gamblers.     

On Consistency of the Self-Consistent Estimator of Survival Functions with Interval-Censored Data

The self-consistent estimator is commonly used for estimating a survival function with interval-censored data. Recent studies on interval censoring have focused on case 2 interval censoring, which does not involve exact observations, and double censoring, which involves only exact, right-censored or left-censored observations. In this paper, we consider an interval censoring scheme that involves exact, left-censored, right-censored and strictly interval-censored observations. Under this censoring scheme, we prove that the self-consistent estimator is strongly consistent under certain regularity conditions.     

《论语》文本研究的几个问题

《论语》的文本形式构成无疑是影响读者对孔子思想作整体性理解的重要因素之一。《论语》文本篇幅之所以如此之少,原因与儒学内部的分化及儒学意识形态化有关;以《论语》为书名是孔子后学对"圣人之言"的价值推崇;"问答"作为《论语》文本的体裁选择是为了通过语境重构来保持文本的开放性,让读者主动参与到对孔子思想的创造性理解;缺乏情景语境的"子曰"式叙事反映了孔子后学对孔子思想进行系统性建构的努力;有子和曾子继承和发挥了孔子"赞述"阐释学传统并从四个方面来构建起《论语》的文本逻辑联系。     

Indirect Inference for Lévy‐driven continuous‐time GARCH models

We advocate the use of an Indirect Inference method to estimate the parameter of a COGARCH(1,1) process for equally spaced observations. This requires that the true model can be simulated and a reasonable estimation method for an approximate auxiliary model. We follow previous approaches and use linear projections leading to an auxiliary autoregressive model for the squared COGARCH returns. The asymptotic theory of the Indirect Inference estimator relies on a uniform strong law of large numbers and asymptotic normality of the parameter estimates of the auxiliary model, which require continuity and differentiability of the COGARCH process with respect to its parameter and which we prove via Kolmogorov's continuity criterion. This leads to consistent and asymptotically normal Indirect Inference estimates under moment conditions on the driving Lévy process. A simulation study shows that the method yields a substantial finite sample bias reduction compared with previous estimators.     

Bayesian Analysis of the Proportional Hazards Model with Time‐Varying Coefficients

We study a Bayesian analysis of the proportional hazards model with time‐varying coefficients. We consider two priors for time‐varying coefficients – one based on B‐spline basis functions and the other based on Gamma processes – and we use a beta process prior for the baseline hazard functions. We show that the two priors provide optimal posterior convergence rates (up to the term) and that the Bayes factor is consistent for testing the assumption of the proportional hazards when the two priors are used for an alternative hypothesis. In addition, adaptive priors are considered for theoretical investigation, in which the smoothness of the true function is assumed to be unknown, and prior distributions are assigned based on B‐splines.     

When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?

Principal component analysis is a popular dimension reduction technique often used to visualize high‐dimensional data structures. In genomics, this can involve millions of variables, but only tens to hundreds of observations. Theoretically, such extreme high dimensionality will cause biased or inconsistent eigenvector estimates, but in practice, the principal component scores are used for visualization with great success. In this paper, we explore when and why the classical principal component scores can be used to visualize structures in high‐dimensional data, even when there are few observations compared with the number of variables. Our argument is twofold: First, we argue that eigenvectors related to pervasive signals will have eigenvalues scaling linearly with the number of variables. Second, we prove that for linearly increasing eigenvalues, the sample component scores will be scaled and rotated versions of the population scores, asymptotically. Thus, the visual information of the sample scores will be unchanged, even though the sample eigenvectors are biased. In the case of pervasive signals, the principal component scores can be used to visualize the population structures, even in extreme high‐dimensional situations.     

默顿科学社会学思想发展的阶段特征

默顿的科学社会学思想发展前后两个时期在理论框架、研究问题、研究对象、研究性质等方面具有不同的特征,特别是其理论框架的不同是最基本的方面.前期运用知识社会学的框架分析了科学发展与其社会文化背景之间的关系,后期从结构功能主义的观点分析了科学制度独立的结构和特征.默顿科学社会学的理论体系包括科学制度的规范和运行两方面内容,不能简单地把它看作是"科学共同体社会学".     

科学社会学的张力——默顿与贝尔纳研究传统的比较

在科学社会学理论中,贝尔纳和默顿分别创建了科学的社会研究中的默顿传统与贝尔纳传统。由于他们在科学知识观上的差异,尽管二者研究前提和研究主旨方面存在一些共同点,但也存在着一定的差别。贝尔纳的立论基础代表了科学家的立场,默顿则坚持的是社会学家的话语。从认识论角度来说,对科学与社会之间的关系,贝尔纳侧重宏观定性研究,默顿及其学派着力于科学内部的社会结构分析,属于微观分析图式。在研究方法上,默顿开辟了一种科学的社会学表述新方法,贝尔纳从科学与社会之间的互动关系出发,提出了一系列科学的社会研究问题。默顿与贝尔纳不同的研究风格,表明了科学社会学研究中存在着巨大的张力。     

基于域映射模式的信息化平台匹配理论研究

从企业信息化应用角度出发,应用域映射理论和方法对管理系统与信息系统关系进行分析,在深入研究企业建模和信息系统开发建模的基础上,提出管理域和信息域的互映射机制、过程和方式,进而提出信息化平台匹配理论,并对映射一致性的测度方法进行研究,以此作为信息化产品选型过程的理论和方法论指导。     

Frequentist model averaging for envelope models

The envelope method produces efficient estimation in multivariate linear regression, and is widely applied in biology, psychology, and economics. This paper estimates parameters through a model averaging methodology and promotes the predicting abilities of the envelope models. We propose a frequentist model averaging method by minimizing a cross-validation criterion. When all the candidate models are misspecified, the proposed model averaging estimator is proved to be asymptotically optimal. When correct candidate models exist, the coefficient estimator is proved to be consistent, and the sum of the weights assigned to the correct models, in probability, converges to one. Simulations and an empirical application demonstrate the effectiveness of the proposed method.