英诗中反复的分类和功能

在英语诗歌众多的艺术手法当中,反复无疑当属运用最广泛之列。从内容上看,反复可分为两类:显性反复和隐性反复;从格式上看,反复也分为两类:紧接反复和间隔反复。反复的功能主要是:渲染感情,烘托气氛;形成节奏,悦耳动听;加强反复部分的意义;整饰诗形等。     

高校思想政治理论课价值功能刍议

由于思想政治理论课价值功能隐性化等原因,其巨大的知识教育、能力培养、育人等方面的价值功能长期以来未能得到应有的认同。为彰显思想政治理论课的价值功能,增强人们认同感,思想政治理论课教育教学必须要有新举措——建立大学生思想政治素质记录袋;要有新局面——加强实践教学环节的制度化建设;要有新思路——强化服务意识。     

"郎当"词义绪

1.归纳汉语北方方言中“郎当”一词之词义,纠正对于该词之误释;2.根据词义起源和词义引申,把该词词义分成两大类:悬挂义和摇摆义及其变体;3.揭示该词的来源:从汉代“锒铛”一词独立出来的动词,并形成了动词词义。     

Instrumental Variable Models for Discrete Outcomes

Single equation instrumental variable models for discrete outcomes are shown to be set identifying, not point identifying, for the structural functions that deliver the values of the discrete outcome. Bounds on identified sets are derived for a general nonparametric model and sharp set identification is demonstrated in the binary outcome case. Point identification is typically not achieved by imposing parametric restrictions. The extent of an identified set varies with the strength and support of instruments, and typically shrinks as the support of a discrete outcome grows. The paper extends the analysis of structural quantile functions with endogenous arguments to cases in which there are discrete outcomes.     

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.     

Measure of location-based estimators in simple linear regression

In this note we consider certain measure of location-based estimators (MLBEs) for the slope parameter in a linear regression model with a single stochastic regressor. The median-unbiased MLBEs are interesting as they can be robust to heavy-tailed samples and, hence, preferable to the ordinary least squares estimator (LSE). Two different cases are considered as we investigate the statistical properties of the MLBEs. In the first case, the regressor and error is assumed to follow a symmetric stable distribution. In the second, other types of regressions, with potentially contaminated errors, are considered. For both cases the consistency and exact finite-sample distributions of the MLBEs are established. Some results for the corresponding limiting distributions are also provided. In addition, we illustrate how our results can be extended to include certain heteroskedastic and multiple regressions. Finite-sample properties of the MLBEs in comparison to the LSE are investigated in a simulation study.     

Bootstrap estimates of the sample bivariate autocorrelation and partial autocorrelation distributions

This paper shows how the bootstrap method can be used to estimate the joint distribution of sample autocorrelations and partial autocorrelations. The exact joint distribution of sample autocorrelations is mathematically intractable and attempts at workable approximations are difficult and rely on special assumptions. The bootstrap offers an accurate solution to this problem without requiring special assumptions and in a way that avoids theoretical difficulties. The bootstrap-estimated joint distributions of the autocorrelations and partial autocorrelations of time series are shown to lead to better ARMA model identification. This is demonstrated using simulated series.     

Population-averaged and subjectspecific approaches for clustered categorical data

Modeling clustered categorical data based on extensions of generalized linear model theory has received much attention in recent years. The rapidly increasing number of approaches suitable for categorical data in which clusters are uncorrelated, but correlations exist within a cluster, has caused uncertainty among applied scientists as to their respective merits and demerits. Upon centering estimation around solving an unbiased estimating function for mean parameters and estimation of covariance parameters describing within-cluster or among-cluster heterogeneity, many approaches can easily be related. This contribution describes a series of algorithms and their implementation in detail, based on a classification of inferential procedures for clustered data.     

Partially linear transformation model for length-biased and right-censored data

In this paper, we consider a partially linear transformation model for data subject to length-biasedness and right-censoring which frequently arise simultaneously in biometrics and other fields. The partially linear transformation model can account for nonlinear covariate effects in addition to linear effects on survival time, and thus reconciles a major disadvantage of the popular semiparamnetric linear transformation model. We adopt local linear fitting technique and develop an unbiased global and local estimating equations approach for the estimation of unknown covariate effects. We provide an asymptotic justification for the proposed procedure, and develop an iterative computational algorithm for its practical implementation, and a bootstrap resampling procedure for estimating the standard errors of the estimator. A simulation study shows that the proposed method performs well in finite samples, and the proposed estimator is applied to analyse the Oscar data.