基于随机森林模型的分类数据缺失值插补

缺失数据是影响调查问卷数据质量的重要因素,对调查问卷中的缺失值进行插补可以显著提高调查数据的质量。调查问卷的数据类型多以分类型数据为主,数据挖掘技术中的分类算法是处理属性分类问题的常用方法,随机森林模型是众多分类算法中精度较高的方法之一。将随机森林模型引入调查问卷缺失数据的插补研究中,提出了基于随机森林模型的分类数据缺失值插补方法,并根据不同的缺失模式探讨了相应的插补步骤。通过与其它方法的实证模拟比较,表明随机森林插补法得到的插补值准确度更优、可信度更高。     

公共政策失灵及其矫正对策

公共政策作为服务和增进公共利益的产品,其本质是社会利益的集中反映。当前,我国公共政策存在失灵现象。矫正公共政策失灵可从如下方面着手：优化政策决策机制;构建信息沟通机制;创新政策执行机制;完善利益均衡机制;加强执行监督机制。     

美国大学的中国留学生助教课堂语用失误案例剖析

美国大学的中国留学生助教在课堂教学中的跨文化交际反映着中国教育的一个侧面。研究以一个助教“nevermind”的语用失误个案出发,从语用学和跨文化交际深入探析其背后反映出来的语言和文化根源。指出语言本身是基于语境的,更根植于其所依存的文化。语言的理解和使用要符合话语行为的基本原则,更要跳出字面理解和母语文化影响的桎梏,看到更大的语言结构和语境。此研究目的是呼吁外语教育从关注纯语言向语言和语用并重的应用型转变,同时希望为在海外从事跨文化教学的群体在本土化教学意识方面提供有益的借鉴。     

MRF和GM模型高光谱图像地物标记

利用马尔可夫随机场和高斯混合模型构造了一种对高光谱图像进行地物标记的新方法。该方法利用PCA降维后的高光谱图像及其差分图像的先验信息建立高光谱图像的随机模型,并把最大后验估计作为地物标记优化的评判标准,用模拟退火算法实现地物标记。实验结果显示该算法是一种精确、高效、稳定的图形标记算法。     

从英汉文化差异看语用翻译

语言文化的个性反应了英汉语言所代表的民族心理意识、文化形成过程、历史习俗传统和地域风貌特征等一系列因素的千差万别,从而导致了英汉语言中诸多的文化差异。英汉文化差异阻碍着跨文化交际的正常进行。语用翻译是一种等效翻译,它可以从语用语言等效和社交语用等效两方面,揭示原作的深层意义,保证跨文化交际的成功。      

On inference for Kendall's τ within a longitudinal data setting

Kendall's τ is a non-parametric measure of correlation based on ranks and is used in a wide range of research disciplines. Although methods are available for making inference about Kendall's τ, none has been extended to modeling multiple Kendall's τs arising in longitudinal data analysis. Compounding this problem is the pervasive issue of missing data in such study designs. In this article, we develop a novel approach to provide inference about Kendall's τ within a longitudinal study setting under both complete and missing data. The proposed approach is illustrated with simulated data and applied to an HIV prevention study.     

Mean estimation with data missing at random for functional covariables

In a missing-data setting, we want to estimate the mean of a scalar outcome, based on a sample in which an explanatory variable is observed for every subject while responses are missing by happenstance for some of them. We consider two kinds of estimates of the mean response when the explanatory variable is functional. One is based on the average of the predicted values and the second one is a functional adaptation of the Horvitz–Thompson estimator. We show that the infinite dimensionality of the problem does not affect the rates of convergence by stating that the estimates are root-n consistent, under missing at random (MAR) assumption. These asymptotic features are completed by simulated experiments illustrating the easiness of implementation and the good behaviour on finite sample sizes of the method. This is the first paper emphasizing that the insensitiveness of averaged estimates, well known in multivariate non-parametric statistics, remains true for an infinite-dimensional covariable. In this sense, this work opens the way for various other results of this kind in functional data analysis.     

Kiefer–Wolfowitz algorithm under quasi-associated random errors

In this article, we study the algorithm of Kiefer–Wolfowitz underquasi-associated random errors. We establish the complete convergence and obtain an exponential bound. Additionally, we build a confidence interval for the minimum. Numerical examples are sketched out to confirm the theoretical results and show the accuracy of the algorithm.