中国居民家庭夫妻收入差异研究

居民家庭是组成整个社会的基本单位,居民家庭夫妻之间的收入差异是了解全社会性别收入差异的重要指标,但却少有研究对此关注。本文采用CHNS2000和2009年数据,对我国居民家庭夫妻间收入差异和全社会性别收入差距进行了动态比较研究,发现夫妻间收入差距远大于全社会不同性别间收入差距,基于Mincer收入方程和Oaxaca分解实证法揭示了“门当户对”观念和“婚姻梯度”现象双重作用下夫妻间收入差距与全社会性别收入差异不同的形成机理,并得出一系列有意义的结论。     

稳健因子分析方法的构建及比较研究

由于传统因子分析方法对离群值较敏感,导致计算结果与实际不相符。针对这一现象,本文运用FAST-MCD方法对传统因子分析方法进行改进,构建出因子分析的稳健算法,以克服离群值的影响,并对此方法进行了模拟和实证分析。模拟和实证分析结果均表明：因子旋转前后,当数据中不存在离群值时,传统因子分析与稳健因子分析得到的结果基本保持一致;当数据中存在离群值时,运用传统因子分析得到的结果出现较大变化,而运用稳健因子分析方法得到的结果基本不变,这说明相对于传统因子分析方法,稳健因子分析方法能有效抵抗离群值的影响,具有良好的抗干扰性和高抗差性。     

Sharp upper mean-variance bounds for trimmed means from restricted families

We apply the method of projection to derive sharp upper mean-variance bounds on trimmed means of order statistics of i.i.d. samples coming from restricted families of probability measures. Two families are considered: distributions with decreasing density and decreasing failure rate. We also present some numerical results.     

中国菲利普斯曲线的非线性特征分析

 在对产出缺口进行测算的基础上,本文运用非线性平滑转换回归模型刻画了我国通货膨胀与产出缺口之间的动态非线性关系。研究结果表明：产出缺口对通货膨胀的影响具有典型的非线性特征和非对称性,并且这种影响具有明显的阶段性特征,在1994-1998年和1999-2007年间,线性特征明显,而在1981-1993年和2008年以后,非线性特征明显,且两种特征之间转换频繁。     

Use of Dummy Variables in Testing for Equality between Sets of Coefficients in Two Linear Regressions: A Note

Elementary inductive proofs are presented for the binomial approximation to the hypergeometric distribution, the density of an order statistic, and the distribution of when X ₁, ···, X _n are a sample from N (μ, 1).     

Practical Point Estimation from Higher-Order Pivots

Point estimators for a scalar parameter of interest in the presence of nuisance parameters can be defined as zero-level confidence intervals as explained in Skovgaard (1989). A natural implementation of this approach is based on estimating equations obtained from higher-order pivots for the parameter of interest. In this paper, generalising the results in Pace and Salvan (1999) outside exponential families, we take as an estimating function the modified directed likelihood. This is a higher-order pivotal quantity that can be easily computed in practice for a wide range of models, using recent advances in higher-order asymptotics (HOA, 2000). The estimators obtained from these estimating equations are a refinement of the maximum likelihood estimators, improving their small sample properties and keeping equivariance under reparameterisation. Simple explicit approximate versions of these estimators are also derived and have the form of the maximum likelihood estimator plus a function of derivatives of the loglikelihood function. Some examples and simulation studies are discussed for widely-used model classes.     

西方“知沟假设”理论评析

文章对西方传播学效果研究的经典理论———“知沟假设”的研究层次进行了深入的分析与评价 ,指出了宏观社会结构层面和个体层面在理论上各自的优势与缺陷 ,尤其从低社会地位群体角度 ,对个体层面无视大众传播的社会后果进行了剖析。文章也从作者在国内所做的大型实证研究出发 ,试图寻求两个研究层次的联结 ,并对“知沟假设”的未来研究进行了构想。     

中国的财政支出与城乡收入差距

 当前我国城乡收入差距正呈现日益扩大的趋势,导致出现这种局面的原因是多方面的,其中,政府的财政投入不足是一个重要方面。但政府增加财政支出是否能有效引导农民收入增长并缩小城乡收入差距呢？我们采用动态面板数据模型和系统广义矩(system-GMM)估计方法的研究表明,这取决于地方政府在追求本地GDP高速增长的政绩最大化条件下,利用自身财政自主权所选择的不同支出结构——在财政分权的背景下,即使财政投入总量增加了,城乡收入差距也未必会缩小,只有倾向于农业投入以及科教文卫支出增加的政府财政支出结构才能有效减轻城乡收入差距。     

Estimation of the bivariate distribution function for censored gap times

In many medical studies, patients may experience several events during follow-up. The times between consecutive events (gap times) are often of interest and lead to problems that have received much attention recently. In this work, we consider the estimation of the bivariate distribution function for censored gap times. Some related problems such as the estimation of the marginal distribution of the second gap time and the conditional distribution are also discussed. In this article, we introduce a nonparametric estimator of the bivariate distribution function based on Bayes’ theorem and Kaplan–Meier survival function and explore the behavior of the four estimators through simulations. Real data illustration is included.     

Clustering confidence sets

In this article, we present a novel approach to clustering finite or infinite dimensional objects observed with different uncertainty levels. The novelty lies in using confidence sets rather than point estimates to obtain cluster membership and the number of clusters based on the distance between the confidence set estimates. The minimal and maximal distances between the confidence set estimates provide confidence intervals for the true distances between objects. The upper bounds of these confidence intervals can be used to minimize the within clustering variability and the lower bounds can be used to maximize the between clustering variability. We assign objects to the same cluster based on a min–max criterion and we separate clusters based on a max–min criterion. We illustrate our technique by clustering a large number of curves and evaluate our clustering procedure with a synthetic example and with a specific application.