Comparative Study of Generalized Estimating Equations and Logistic Regressions on Different Sample Sizes and Correlation Levels

In this study, it was aimed to determine accuracy of generalized estimating equations versus logistic regressions on different correlation levels and sample sizes. For this aim, two methods were compared with different sample sizes 10, 25, 50 and 100 and correlation levels 0.0, 0.3, 0.5 and 0.8. Result of this study showed that using generalized estimating equations could be preferred versus logistic regression when the sample size is over than 25 and correlation level is higher than 0.3 on data taken from studies with repeated measurements, but logistic regression could be better when the autocorrelations do not exist.     

Statistical inference based on generalized Lindley record values

This paper addresses the problems of frequentist and Bayesian estimation for the unknown parameters of generalized Lindley distribution based on lower record values. We first derive the exact explicit expressions for the single and product moments of lower record values, and then use these results to compute the means, variances and covariance between two lower record values. We next obtain the maximum likelihood estimators and associated asymptotic confidence intervals. Furthermore, we obtain Bayes estimators under the assumption of gamma priors on both the shape and the scale parameters of the generalized Lindley distribution, and associated the highest posterior density interval estimates. The Bayesian estimation is studied with respect to both symmetric (squared error) and asymmetric (linear-exponential (LINEX)) loss functions. Finally, we compute Bayesian predictive estimates and predictive interval estimates for the future record values. To illustrate the findings, one real data set is analyzed, and Monte Carlo simulations are performed to compare the performances of the proposed methods of estimation and prediction.     

A comparison study on modeling of clustered and overdispersed count data for multiple comparisons

Data collected in various scientific fields are count data. One way to analyze such data is to compare the individual levels of the factor treatment using multiple comparisons. However, the measured individuals are often clustered – e.g. according to litter or rearing. This must be considered when estimating the parameters by a repeated measurement model. In addition, ignoring the overdispersion to which count data is prone leads to an increase of the type one error rate. We carry out simulation studies using several different data settings and compare different multiple contrast tests with parameter estimates from generalized estimation equations and generalized linear mixed models in order to observe coverage and rejection probabilities. We generate overdispersed, clustered count data in small samples as can be observed in many biological settings. We have found that the generalized estimation equations outperform generalized linear mixed models if the variance-sandwich estimator is correctly specified. Furthermore, generalized linear mixed models show problems with the convergence rate under certain data settings, but there are model implementations with lower implications exists. Finally, we use an example of genetic data to demonstrate the application of the multiple contrast test and the problems of ignoring strong overdispersion.     

Lower bound of average centered L2-discrepancy for U-type designs

Uniform designs are widely used in various scientific investigations and industrial applications. By considering all possible level permutation of the factors, a connection between average centered L₂-discrepancy and generalized wordlength pattern for asymmetrical fractional factorial designs is derived. Moreover, we present new lower bounds to the average centered L₂-discrepancy for symmetrical and asymmetrical U-type designs. For illustration of the theoretical results, the lower bounds for symmetrical and asymmetrical U-type designs are tabulated, and numerical results indicate that our lower bounds behave well and can be recommended for use in practice.     

Instrumental variable based variable selection for generalized linear models with endogenous covariates

We study the variable selection problem for a class of generalized linear models with endogenous covariates. Based on the instrumental variable adjustment technology and the smooth-threshold estimating equation (SEE) method, we propose an instrumental variable based variable selection procedure. The proposed variable selection method can attenuate the effect of endogeneity in covariates, and is easy for application in practice. Some theoretical results are also derived such as the consistency of the proposed variable selection procedure and the convergence rate of the resulting estimator. Further, some simulation studies and a real data analysis are conducted to evaluate the performance of the proposed method, and simulation results show that the proposed method is workable.     

Inferences for a Partially Varying Coefficient Model With Endogenous Regressors

In this article, we propose a new class of semiparametric instrumental variable models with partially varying coefficients, in which the structural function has a partially linear form and the impact of endogenous structural variables can vary over different levels of some exogenous variables. We propose a three-step estimation procedure to estimate both functional and constant coefficients. The consistency and asymptotic normality of these proposed estimators are established. Moreover, a generalized F-test is developed to test whether the functional coefficients are of particular parametric forms with some underlying economic intuitions, and furthermore, the limiting distribution of the proposed generalized F-test statistic under the null hypothesis is established. Finally, we illustrate the finite sample performance of our approach with simulations and two real data examples in economics.     

论魏晋南北朝的同题共作赋

同题共作属于集体性的创作活动,赋的同题共作以魏晋南北朝为盛,这与当时文学集团的兴盛有直接关系。同一命题、相同环境下的创作,及其所固有的切磋品评的创作目的,使同题共作赋呈现出相反相成的两种倾向:一是题材范围和艺术风格上的趋同,二是同中求异的竞胜争高。这种方式的创作对于赋体文学表现领域的拓展和艺术技巧的提高都产生了重要作用。魏晋南北朝同题共作赋的文学史意义,则主要表现在促进了短赋的发展,并加快了多诗少赋的文学进程。     

形似与神似，求真与求美--从本体诗歌翻译理论的角度分析

本体诗歌翻译理论主要探讨译文与原文在形式与内容上的关系。译文与原文应该达到形似还是神似,译者应该求真还是求美,这是翻译界一直争论的话题。按照“形似与神似,求真与求美”的关系来分析江枫先生对艾米莉·狄金森的两首诗歌“暴风雨夜”和“篱笆那边”的翻译,可以看出,标点符号、遣词、诗歌的韵式和诗人的创作风格等方面体现了江枫译文的特点和风格：译文选词简单但富有张力,行文简约而内敛,与原诗风格颇似;但由于过于追求简约而稍显生硬和晦涩。总之,江枫的译文是形似与神似结合的典范,体现其“形似而后神似”的主张以及“简约而忠实”的风格,但并未完全实现“求真与求美”的完美结合。     

隐喻的文化认知及英汉表述

因为相同的文化认知方式,英汉两种语言会有相同的隐喻概念;但因为英汉同时也有不同的文化认知方式,所以这两种语言之间也会有不同的隐喻概念。在翻译时如文化认知不同时,可用目标语的隐喻形象来替换原文中的隐喻形象,如文化认知相同时,可重视原文的隐喻形象又传达原文表达的意义。     

汉、英动物谚语比较

本文从不同角度对比分析了汉、英动物谚语。对比结果表明:汉、英两个民族的文化传统、生产活动及所处的自然环境等方面不尽相同,这造成了汉、英动物谚语在所涉及的动物种类、动物词语的使用频率以及它们的象征意义等方面的差异。