论强纲领中的因果性模型问题

布鲁尔强纲领中所反对的目的论模型来源于巴恩斯提出的目的论模式，他们都拒绝将科学的发展看作真理进化过程，认为作为一种信念的科学知识应当纳入知识社会学的研究领域。布鲁尔进一步提出了与目的论模型相对立的因果性模型，并构建出科学知识社会学应当遵守的四条“强纲领”，力图促使知识社会学成为一门普遍性的学科。布鲁尔声称强纲领的因果性模型与目的论模型是两种相互排斥的形而上学立场，但实际上因果性模型与目的论模型并不完全排斥，甚至还有所交融；同时，他关于因果性模型的论证与辩护也存在诸多问题，是一种不彻底的“强纲领”。     

技术特征、关系结构与社会化购买行为

社会化媒体技术与社会关系网络促进了社会化商务(SC)的价值创造，如何有效利用技术与用户的关系结构是 SC 企业提高其平台购买转化率的关键． 从双重视角探索技术与关系如何影响用户购买决策，基于刺激―机体―反应框架，构建了以技术可供性和强、弱关系为刺激因素，信息诊断性和意外发现性为内在机体状态，社会化购买意向为“反应”的理论模型，探索了用户社会化购买行为的发生机制． 以具有社会化购买体验的微信用户作为研究对象，运用 SmartPLS 3.0 对模型进行了路径检验和假设分析． 研究结果发现: 技术可供性与弱关系对信息诊断性和信息意外发现性具有正向影响; 弱关系对信息意外发现性的影响作用大于强关系; 信息诊断性正向影响信息意外发现性，信息诊断性与意外发现性对社会化购买意向均有显著正向影响; 然而，强关系对信息意外发现性的影响不显著． 信息诊断性与信息意外发现性部分中介了技术可供性对社会化购买意向的影响，完全中介了弱关系对社会化购买意向的影响，信息诊断性部分中介了强关系对社会化购买意向的影响． 为进一步探究社会化购买行为提供 了理论基础和实践指导．     

Humor and satire in politics: Introducing cultural sociology to the field

The landscape of political humor and satire is changing rapidly, and it is becoming an increasingly relevant aspect of our culture. Although scholars have been actively trying to capture this change, majority of the existing frameworks for understanding humor and satire in politics still reduce these phenomena to mere genres or rhetoric tools. In addition, they provide insufficient accounts concerning the reception of humor and satire, and neglect to interpret and explain what they communicate. In the article, a general understanding of humor and satire outside of a political context, followed by an overview of studies discussing humor and satire in politics through their applications in social movements, as leadership tools, and through their manifestations in mass media has been presented. Lastly, a cultural sociological perspective to the field has been introduced. It has been argued that approaching humor and satire in politics through a structural hermeneutic method of the Strong Program will enable us to recognize and treat political humor and satire as autonomous and complex cultural systems which carry an internal power to move people.     

Uniform consistency rate of kNN regression estimation for functional time series data

In this paper, we investigate the k-nearest neighbours (kNN) estimation of nonparametric regression model for strong mixing functional time series data. More precisely, we establish the uniform almost complete convergence rate of the kNN estimator under some mild conditions. Furthermore, a simulation study and an empirical application to the real data analysis of sea surface temperature (SST) are carried out to illustrate the finite sample performances and the usefulness of the kNN approach.     

Local asymptotic normality for long-memory process with strong mixing noises

Abstract

We will establish the local asymptotic normality (LAN) for fractional autoregressive long memory model in the case of strong mixing noises. This opens the way in future work to construct an adaptive estimator and construct optimal tests for the parameters. To check the feasibility and validity of our theoretical results a simulations study is considered.     

Complete convergence and the strong laws of large numbers for pairwise NQD random variables

The authors study the strong convergence for sequences of pairwise negatively quadrant dependent (NQD) random variables under some wide conditions, and present some new theorems on the complete convergence and the strong laws of large numbers. The obtained results extend and improve some theorems in existing literature.     

马克思自由思想视域下人工智能诠释的三个维度

人工智能技术的出现，表现出对自由的张力和限制，亟需以合理的视角审视人工智能，为人工智能的应用正本清源。事实上，马克思自由观为诠释人工智能提供了立体化呈现维度。以自由劳动之维观察，人工智能下的劳动消解传统分工，使劳动复归自觉，具有科学性和社会性动向；以自由时间之维观察，人工智能延伸了通往自由王国的自由时间；以自由个性之维观察，人工智能的终极指向是生成自由自觉的类存在，这三个诠释维度之间彼此有着密切的关联。在正视人工智能时，也要警醒人工智能技术在资本逻辑驱动下容易成为异化人和削弱人本质力量的工具，只有这样才能从人工智能的变革中挖掘人自由全面发展的价值。     

The Jackknife estimate of covariance of two Kaplan–Meier integrals with covariables

In this paper the Jackknife estimate of covariance of two Kaplan–Meier integrals with covariates is introduced. Its strong consistency is established under mild conditions. Several applications of the estimator are discussed.     

Simultaneous sparse model selection and coefficient estimation for heavy-tailed autoregressive processes

We propose a sparse coefficient estimation and automated model selection procedure for autoregressive processes with heavy-tailed innovations based on penalized conditional maximum likelihood. Under mild moment conditions on the innovation processes, the penalized conditional maximum likelihood estimator satisfies a strong consistency, O_P(N^?1/2) consistency, and the oracle properties, where N is the sample size. We have the freedom in choosing penalty functions based on the weak conditions on them. Two penalty functions, least absolute shrinkage and selection operator and smoothly clipped average deviation, are compared. The proposed method provides a distribution-based penalized inference to AR models, which is especially useful when the other estimation methods fail or under perform for AR processes with heavy-tailed innovations [Feigin, Resnick. Pitfalls of fitting autoregressive models for heavy-tailed time series. Extremes. 1999;1:391–422]. A simulation study confirms our theoretical results. At the end, we apply our method to a historical price data of the US Industrial Production Index for consumer goods, and obtain very promising results.     

The almost sure convergence rate of the estimator of optimized certainty equivalent risk measure under α-mixing sequences

Conditional value-at-risk (CVaR) model is a kind of financial risk measure that is extensively supported and accepted by international financial community. Its optimized form can be regarded as an optimized certainty equivalent (OCE) risk measurement. In this paper, we mainly discuss and analyze the strong laws of large numbers and the convergence rate of OCE's estimator under α-mixing sequences. The result shows that the almost sure convergence rate of CVaR estimator is given by the results of OCE estimator. Its convergence rate is inversely proportional to the square root of the sample size under certain conditions. Its effectiveness is verified by simulation experiments for two classical α-mixing sequences.