认知语境的构建

本文在关联理论的框架下讨论发话人怎袢通过语言和非语言手段制约受话人的认知语境以及受话人如何在认知环境中选择并延伸认知语境.     

浅谈语境与交际

本文从语用学角度出发探讨了语境与交际的关系.重点论述了人们在交际过程中,在交际模式的指导下如何利用语境,寻找到最佳的关联,推理出说话者的真正意图,具体分析了语境在交际中所起的重大作用.     

模糊取式推理的R0方法

用R0蕴涵算子描述了模糊命题“若A则B”,并通过一个例子给出了运用模糊取式推理的R0方法进行推理的具体操作过程．结果表明,运用该模糊推理得到的结论是与人们的思想相吻合的．     

Grice语用学模式批判

在话语理解中 ,通过解码获得语义 ,通过推理得出说话人意图。在语用学的发展过程中 ,话语理解的重心逐渐转移到推理过程 ,Grice的推理模式就是这种发展的代表 ,认为交际中人们遵守着合作原则以及相关准则 ,违背某些准则是为了表达暗含意义 ,需要推理求得理解。然而 ,Grice过分强调暗含意义 ,而且合作原则及有关准则来源不清 ,严重地削弱了Grice推理模式的解释力。     

统计学发展过程中四次重要的争论及其影响

统计学发展过程中出现过几次重大的争论,这些争论在本质上可以说是一脉相承的。"政治算术"与"国势学"的争论,明确了统计学的学科性质;"描述统计学"与"推断统计学"的争论,构筑了统计学的完整体系;"经典统计学"与"贝叶斯统计学"的争论,带来了统计哲学观的新变化;信念统计学与经典统计学、贝叶斯统计学的争论,使统计推断科学化问题的研究日趋深入。正是通过这些争论完善了现代统计学的思想和方法体系。     

Marginal Regression of Gaps Between Recurrent Events

Recurrent event data typically exhibit the phenomenon of intra-individual correlation, owing to not only observed covariates but also random effects. In many applications, the population may be reasonably postulated as a heterogeneous mixture of individual renewal processes, and the inference of interest is the effect of individual-level covariates. In this article, we suggest and investigate a marginal proportional hazards model for gaps between recurrent events. A connection is established between observed gap times and clustered survival data with informative cluster size. We subsequently construct a novel and general inference procedure for the latter, based on a functional formulation of standard Cox regression. Large-sample theory is established for the proposed estimators. Numerical studies demonstrate that the procedure performs well with practical sample sizes. Application to the well-known bladder tumor data is given as an illustration.     

时间贝叶斯网络及其概率推理

针对贝叶斯网络应用中出现的循环和动态问题,研究贝叶斯网络的时间扩展.给出了时间贝叶斯网络的定义;探讨了时间贝叶斯网络中环的存在合理性判断问题;给出了时间贝叶斯网络的概率推理算法,应用示例说明了方法的可行性.     

Criticism of the predictive distribution

The predictive distribution is a mixture of the original distribution model and is used for predicting a future observation. Therein, the mixing distribution is the posterior distribution of the distribution parameters in the Bayesian inference. The mixture can also be computed for the frequentist inference because the Bayesian posterior distribution has the same meaning as a frequentist confidence interval. I present arguments against the concept of predictive distribution. Examples illustrate these. The most important argument is that the predictive distribution can depend on the parameterization. An improvement of the theory of the predictive distribution is recommended.     

Acute Middle Ear Infection in Small Children: A Bayesian Analysis Using Multiple Time Scales

The study is based on a sample of 965 children living in Oulu region (Finland), who were monitored for acute middle ear infections from birth to the age of two years. We introduce a nonparametrically defined intensity model for ear infections, which involves both fixed and time dependent covariates, such as calendar time, current age, length of breast-feeding time until present, or current type of day care. Unmeasured heterogeneity, which manifests itself in frequent infections in some children and rare in others and which cannot be explained in terms of the known covariates, is modelled by using individual frailty parameters. A Bayesian approach is proposed to solve the inferential problem. The numerical work is carried out by Monte Carlo integration (Metropolis-Hastings algorithm).     

Program Evaluation and Causal Inference With High‐Dimensional Data

In this paper, we provide efficient estimators and honest confidence bands for a variety of treatment effects including local average (LATE) and local quantile treatment effects (LQTE) in data‐rich environments. We can handle very many control variables, endogenous receipt of treatment, heterogeneous treatment effects, and function‐valued outcomes. Our framework covers the special case of exogenous receipt of treatment, either conditional on controls or unconditionally as in randomized control trials. In the latter case, our approach produces efficient estimators and honest bands for (functional) average treatment effects (ATE) and quantile treatment effects (QTE). To make informative inference possible, we assume that key reduced‐form predictive relationships are approximately sparse. This assumption allows the use of regularization and selection methods to estimate those relations, and we provide methods for post‐regularization and post‐selection inference that are uniformly valid (honest) across a wide range of models. We show that a key ingredient enabling honest inference is the use of orthogonal or doubly robust moment conditions in estimating certain reduced‐form functional parameters. We illustrate the use of the proposed methods with an application to estimating the effect of 401(k) eligibility and participation on accumulated assets. The results on program evaluation are obtained as a consequence of more general results on honest inference in a general moment‐condition framework, which arises from structural equation models in econometrics. Here, too, the crucial ingredient is the use of orthogonal moment conditions, which can be constructed from the initial moment conditions. We provide results on honest inference for (function‐valued) parameters within this general framework where any high‐quality, machine learning methods (e.g., boosted trees, deep neural networks, random forest, and their aggregated and hybrid versions) can be used to learn the nonparametric/high‐dimensional components of the model. These include a number of supporting auxiliary results that are of major independent interest: namely, we (1) prove uniform validity of a multiplier bootstrap, (2) offer a uniformly valid functional delta method, and (3) provide results for sparsity‐based estimation of regression functions for function‐valued outcomes.