具有部分缺失数据时两个威布尔分布的估计和检验

文中讨论了部分缺失数据两威布尔总体的参数估计和关于总体相同的似然比检验．证明估计的强相合性和渐近正态性,给出似然比检验统计量的极限分布,并探讨了基于精确分布的检验问题．     

Estimation of Item Response Theory Parameters in the Presence of Missing Data

Missing data are a common problem in a variety of measurement settings, including responses to items on both cognitive and affective assessments. Researchers have shown that such missing data may create problems in the estimation of item difficulty parameters in the Item Response Theory (IRT) context, particularly if they are ignored. At the same time, a number of data imputation methods have been developed outside of the IRT framework and been shown to be effective tools for dealing with missing data. The current study takes several of these methods that have been found to be useful in other contexts and investigates their performance with IRT data that contain missing values. Through a simulation study, it is shown that these methods exhibit varying degrees of effectiveness in terms of imputing data that in turn produce accurate sample estimates of item difficulty and discrimination parameters.     

缺失数据下遗传重组率估计的EM方法

EM算法是在不完全信息资料下实现参数估计的一种通用迭代方法，其在现代科学的许多领域已有着广泛的应用。文章导出了双位点不同标记类型，包括共显性-共显性，共显性-显性和显性-显性三种模式下，部分个体缺失标记基因型时，重组率估计率的EM算法。用编制的SAS／IML程序进行了Monte Carlo模拟研究，验证了文章所述方法在遗传连锁分析中的有效性和实用性。     

因变量缺失的变系数部分线性模型的有效估计

讨论了因变量随机缺失条件下变系数部分线性模型的估计问题。基于局部借补思想,使用局部线性方法和平均技巧同时得到了各个估计量的估计,进而给出了估计的渐近性质。     

缺失数据的处理和挑战

在数据挖掘研究中,缺失数据是一个非常普遍的问题,如何处理缺失数据也是一个热门的研究领域．介绍了缺失数据产生的原因,分类总结了缺失数据的处理方法,最后,提出了处理缺失数据的一些挑战性课题。     

两个0-1总体具有部分缺失数据时的估计和检验

文章主要讨论了两个0-1总体均具有部分缺失数据时的参数的极大似然估计,并证明了估计的强相合性和渐进正态性,给出了大样本场合下的似然比检验统计量的极限分布。     

数据非随机缺失机制的检验

本文采用一种模拟算法对数据非随机缺失(NMAR)机制的检验问题作了初步的探讨.并用一个例子说明NMAR机制的检验问题的合理性.     

不同背景颜色对阅读理解的影响

目的:探讨不同背景颜色对大学生阅读理解的影响情况。方法:从福建某大学选取160名大一学生为被试,采用4×2被试间设计,以ppt的形式呈现中文材料阅读,以此考察大学生的阅读成绩。结果:(1)不同背景颜色存在显著差异(F=4.731,p=.003),其中白色背景的阅读成绩高于红、绿、蓝背景;(2)字体大小差异不显著(F=1.104,p=.295),背景色和字体大小交互作用不显著(F=2.156,p=.096)。结论:颜色对阅读成绩产生影响,字体大小对阅读成绩不产生影响。     

Graphical Representation of Missing Data Problems

Rubin’s classic missingness mechanisms are central to handling missing data and minimizing biases that can arise due to missingness. However, the formulaic expressions that posit certain independencies among missing and observed data are difficult to grasp. As a result, applied researchers often rely on informal translations of these assumptions. We present a graphical representation of missing data mechanism, formalized in Mohan, Pearl, and Tian (2013). We show that graphical models provide a tool for comprehending, encoding, and communicating assumptions about the missingness process. Furthermore, we demonstrate on several examples how graph-theoretical criteria can determine if biases due to missing data might emerge in some estimates of interests and which auxiliary variables are needed to control for such biases, given assumptions about the missingness process.     

Dealing With Item Nonresponse in Large‐Scale Cognitive Assessments: The Impact of Missing Data Methods on Estimated Explanatory Relationships

Competence data from low‐stakes educational large‐scale assessment studies allow for evaluating relationships between competencies and other variables. The impact of item‐level nonresponse has not been investigated with regard to statistics that determine the size of these relationships (e.g., correlations, regression coefficients). Classical approaches such as ignoring missing values or treating them as incorrect are currently applied in many large‐scale studies, while recent model‐based approaches that can account for nonignorable nonresponse have been developed. Estimates of item and person parameters have been demonstrated to be biased for classical approaches when missing data are missing not at random (MNAR). In our study, we focus on parameter estimates of the structural model (i.e., the true regression coefficient when regressing competence on an explanatory variable), simulating data according to various missing data mechanisms. We found that model‐based approaches and ignoring missing values performed well in retrieving regression coefficients even when we induced missing data that were MNAR. Treating missing values as incorrect responses can lead to substantial bias. We demonstrate the validity of our approach empirically and discuss the relevance of our results.     

The Impact of Background Television on Parent–Child Interaction

This study investigated the hypothesis that background television affects interactions between parents and very young children. Fifty-one 12-, 24-, and 36-month-old children, each accompanied by 1 parent, were observed for 1 hr of free play in a laboratory space resembling a family room. For half of the hour, an adult-directed television program played in the background on a monaural television set. During the other half hour, the television was not on. Both the quantity and quality of parent–child interaction decreased in the presence of background television. These findings suggest one way in which early, chronic exposure to television may have a negative impact on development.     

Handling Missing Data in the Modeling of Intensive Longitudinal Data

Myriad approaches for handling missing data exist in the literature. However, few studies have investigated the tenability and utility of these approaches when used with intensive longitudinal data. In this study, we compare and illustrate two multiple imputation (MI) approaches for coping with missingness in fitting multivariate time-series models under different missing data mechanisms. They include a full MI approach, in which all dependent variables and covariates are imputed simultaneously, and a partial MI approach, in which missing covariates are imputed with MI, whereas missingness in the dependent variables is handled via full information maximum likelihood estimation. We found that under correctly specified models, partial MI produces the best overall estimation results. We discuss the strengths and limitations of the two MI approaches, and demonstrate their use with an empirical data set in which children’s influences on parental conflicts are modeled as covariates over the course of 15 days (Schermerhorn, Chow, & Cummings, 2010).     

On Testability of Missing Data Mechanisms in Incomplete Data Sets

This article is concerned with the question of whether the missing data mechanism routinely referred to as missing completely at random (MCAR) is statistically examinable via a test for lack of distributional differences between groups with observed and missing data, and related consequences. A discussion is initially provided, from a formal logic standpoint, of the distinction between necessary conditions and sufficient conditions. This distinction is used to argue then that testing for lack of these group distributional differences is not a test for MCAR, and an example is given. The view is next presented that the desirability of MCAR has been frequently overrated in empirical research. The article is finalized with a reference to principled, likelihood-based methods for analyzing incomplete data sets in social and behavioral research.     

A Review of Methods for Missing Data

This paper reviews methods for handling missing data in a research study. Many researchers use ad hoc methods such as complete case analysis, available case analysis (pairwise deletion), or single-value imputation. Though these methods are easily implemented, they require assumptions about the data that rarely hold in practice. Model-based methods such as maximum likelihood using the EM algorithm and multiple imputation hold more promise for dealing with difficulties caused by missing data. While model-based methods require specialized computer programs and assumptions about the nature of the missing data, these methods are appropriate for a wider range of situations than the more commonly used ad hoc methods. The paper provides an illustration of the methods using data from an intervention study designed to increase students’ ability to control their asthma symptoms.     

缺失数据的插补调整方法

统计调查中,经常会遇到数据缺失的现象．缺失数据会影响统计分析的质量．文中提出了一种先对数据的缺失机制进行统计检验,针对是否存在显著分布偏性,分别采取相应的插补算法的统计方法．该系统算法可以全面处理离散数据和时间序列数据的不同缺失机制的缺失数据．     

数据库索引对数据操作的影响

在SQL Server表上使用索引对数据操作性能非常关键,有效地设计索引可以提高SQL Serv-er系统的性能。不合理的使用索引不仅会使表的操作速度明显变慢,而且会使数据检索的速度降低。因此,要创建合适的索引以提高数据库的性能。     

文化背景对翻译的影响

翻译既是跨语言的活动,也是跨文化的活动,语言与文化之间具有不可分割的关系.没有语言,文化就不可能存在,语言也只有反映文化才有意义.翻译过程中,译者不但要精通两国的语言,还必须具有深厚的文化功底,能深刻理解两种文化的异同.只有这样,才能做到不仅语言意义上等值,而且文化意义上等值.文章从文化背景对翻译的影响入手,分析了正确处理两者关系的方法.     

背景音乐对学习的影响研究

从背景音乐对学习的影响,背景音乐对认知的影响,背景音乐影响作用的生理、心理机制四方面探讨背景音乐对学习和认知的影响,同时针对研究中的结果提出教学建议,以便学校能够为学生创设良好的学习情境,为提高教与学的效率提供依据。     

大数据背景下公共数据资源运用探析

我国数据资源主要集中于政府部门手中,这会诱发一系列问题,一方面,政府部门将数据资源束之高阁导致数据资源严重浪费,另一方面,公共数据资源开发力度不够以及公共数据开放中导致公民信息泄露问题都成为制约大数据发展的重要障碍.因此,建立健全大数据相关运用立法、加强数据运用监管、提高政府部门和社会公众的数据意识,形成数据文化,是推动大数据产业发展所必需.