2004年诺贝尔经济学奖得主的理论贡献

芬·基德兰德和爱德华·普雷斯考特因在实际经济周期理论和经济政策的时间一致性方面的研究成果获得了2004年诺贝尔经济学奖,本文介绍分析了获奖者在这两个方面的理论贡献。     

论2004年诺贝尔经济奖对我国宏观经济政策的启示

2004年诺贝尔经济学奖的获奖成果主要体现在两个方面,即对宏观经济政策的“时间连贯性难题”和商业周期影响因素的研究。文章较详尽深入地介绍了这两个理论的研究方法和研究成果,并指出两位获奖者的贡献不仅体现在学术上,而且对很多国家的宏观经济政策的制定也有着重要借鉴意义,提出他们的研究成果对我国宏观经济政策的启示:改善政府的宏观调控和重视科技进步对经济的推动作用。     

顾客认知对品牌心理所有权的影响研究——基于品牌价值观一致性的中介作用

随着社会化媒体的快速发展,顾客选择日趋多样化,提升顾客品牌心理所有权成为理论和实践共同关注的重要课题。虽然学术界关于品牌心理所有权的概念和维度仍存在些许分歧,但品牌心理所有权对品牌及企业的重要性已得到广泛认可。基于社会心理学原理,以虚拟社区为载体实证分析顾客认知因素、品牌价值观一致性、品牌心理所有权三者之间的内在关系,有助于企业深入了解顾客对品牌心理所有权的影响因素,为企业制定正确的营销战略提供理论与实践指导。     

认知语言学的经验观、突显观、注意观及其一致性

认知语言学被分为经验观、突显观和注意观。经验观认为语言研究要做实验和研究日常语言。突显观主要讨论图形/背景分离理论在介词词义分析和句法结构分析中的应用。注意观根据认知主体在组织信息时注意力的分配研究句法结构。但三种路向的划分夸大了它们之间的区别而忽视了其内在联系。三种路向在本质上是一致的。     

Nonparametric estimation of the lower tail dependence λL in bivariate copulas

The lower tail dependence λ_L is a measure that characterizes the tendency of extreme co-movements in the lower tails of a bivariate distribution. It is invariant with respect to strictly increasing transformations of the marginal distribution and is therefore a function of the copula of the bivariate distribution. λ_L plays an important role in modelling aggregate financial risk with copulas. This paper introduces three non-parametric estimators for λ_L. They are weakly consistent under mild regularity conditions on the copula and under the assumption that the number k = k(n) of observations in the lower tail, used for estimation, is asymptotically k ≈ √n. The finite sample properties of the estimators are investigated using a Monte Carlo simulation in special cases. It turns out that these estimators are biased, where amount and sign of the bias depend on the underlying copula, on the sample size n, on k, and on the true value of λ_L.     

Marginal Regression for Binary Longitudinal Data in Adaptive Clinical Trials

Abstract.  In an adaptive clinical trial research, it is common to use certain data-dependent design weights to assign individuals to treatments so that more study subjects are assigned to the better treatment. These design weights must also be used for consistent estimation of the treatment effects as well as the effects of the other prognostic factors. In practice, there are however situations where it may be necessary to collect binary responses repeatedly from an individual over a period of time and to obtain consistent estimates for the treatment effect as well as the effects of the other covariates in such a binary longitudinal set up. In this paper, we introduce a binary response-based longitudinal adaptive design for the allocation of individuals to a better treatment and propose a weighted generalized quasi-likelihood approach for the consistent and efficient estimation of the regression parameters including the treatment effects.     

Goodness‐of‐fit tests for parametric models based on biased samples

The authors study the problem of checking the adequacy of a parametric model for a distribution using several possibly censored weight biased samples. They discuss identifiability problems related to the underlying distribution and the distributions of the biased samples. They propose a test statistic based on the supremum of the weighted aggregated martingale residual processes from a number of such samples. Both numerical and graphical procedures are discussed, which the authors apply to do model checking for oil exploration drilling data.     

群组AHP排序的最小最大几何距离方法

对群组判断矩阵提出一种新的最小最大几何距离排序方法(MGDM)。鉴于不同专家所给判断矩阵质量上的差异,MGDM对群组判断矩阵进行不同程度的加权处理,并进行群组一致性检验。     

伽马分布的形状参数的一种估计

本文利用服从伽马分布的随机变量 X 与其逆 X~(-1)的协方差只与形状参数有关这一性质,给出伽马分布形状参数的所谓自逆协方差估计,进而构造了相应的无偏估计,并证明了这类估计的大样本性质:强相合性以及渐近正态性。     

The strong uniform convergence of multivariate variable kernel estimates

We show that sup, completely as, where f is a uniformly continuous density on are independent random vectors with common density f, and f_n is the variable kernel estimate Here H_ni is the distance between X_i and its kth nearest neighbour, K is a given density satisfying some regularity conditions, and k is a sequence of integers with the property that log asn