共查询到19条相似文献，搜索用时 455 毫秒
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介绍了统计可变形模型的建立过程和使用建立的统计模型来恢复缺失数据的算法.并将该算法用于肝脏CT图像实验了三种不同分辨率下的恢复效果,结果表明:使用该方法恢复肝脏CT图像,可将误差控制在1%以下. 相似文献
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文中吸收了诸多学者的研究成果,利用logit模型刻画缺失指示变量R的分布．对于一组有缺失的数据,首先假定出R的分布,然后估计出分布中的参数,最后利用得到的参数估计来判定数据的缺失机制类型． 相似文献
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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. 相似文献
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在社会资料调查工作中,往往因失访、未响应或者回馈结果不合格等多方面因素,会造成不同程度上的数据缺失,这一现象较为常见且难以避免.按照统计学观点,将包含缺失数据的记录归为不完全观测范畴,它对调查研究工作的最终结果影响巨大.而EM(期望最大化)算法,则恰好满足了需要利用不完全数据来研究分析问题的要求,具有重要意义.本文结合不同缺失率下的EM算法参数估计分析,对EM算法进行了全面评测. 相似文献
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测试目标：1．理解平均数、中位数和众数的统计意义,会求一组数据的中位数和众数;2．会计算加权平均数,理解“权”的意义,能选择适当的统计量表示数据的集中趋势． 相似文献
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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． 相似文献
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教学内容北师大版《义务教育课程标准实验教科书·数学》五年级下册“中位数和众数”。
教学目标 1．在实际情境中。认识并会求一组数据的中位数、众数,并解释其实际意义。2．根据具体的问题．能选择适当的统计量表示数据的不同特征。3．感受统计在生活中的应用．增强统计意识。发展统计观念。 相似文献
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吕虹 《贵州教育学院学报》2013,(9):22-25
随着计算机技术、通信技术和互联网的高速发展,网络调查进入到了快速发展的阶段。针对所设计的调查系统题目开放易存在缺失数据值的问题,应用了最大期望的贝叶斯网络算法对缺失数据值进行填充,利用填充后的完备数据集进行分类别统计分析,分析结果表明网络调查与印刷调查可以得到一致的调查结果。 相似文献
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Shelley A. Blozis Xiaojia Ge Shu Xu Misaki N. Natsuaki Daniel S. Shaw Jenae M. Neiderhiser 《Structural equation modeling》2013,20(2):283-298
Missing data are common in studies that rely on multiple informant data to evaluate relationships among variables for distinguishable individuals clustered within groups. Estimation of structural equation models using raw data allows for incomplete data, and so all groups can be retained for analysis even if only 1 member of a group contributes data. Statistical inference is based on the assumption that data are missing completely at random or missing at random. Importantly, whether or not data are missing is assumed to be independent of the missing data. A saturated correlates model that incorporates correlates of the missingness or the missing data into an analysis and multiple imputation that might also use such correlates offer advantages over the standard implementation of SEM when data are not missing at random because these approaches could result in a data analysis problem for which the missingness is ignorable. This article considers these approaches in an analysis of family data to assess the sensitivity of parameter estimates and statistical inferences to assumptions about missing data, a strategy that could be easily implemented using SEM software. 相似文献
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The purpose of this study is to investigate the effects of missing data techniques in longitudinal studies under diverse conditions. A Monte Carlo simulation examined the performance of 3 missing data methods in latent growth modeling: listwise deletion (LD), maximum likelihood estimation using the expectation and maximization algorithm with a nonnormality correction (robust ML), and the pairwise asymptotically distribution-free method (pairwise ADF). The effects of 3 independent variables (sample size, missing data mechanism, and distribution shape) were investigated on convergence rate, parameter and standard error estimation, and model fit. The results favored robust ML over LD and pairwise ADF in almost all respects. The exceptions included convergence rates under the most severe nonnormality in the missing not at random (MNAR) condition and recovery of standard error estimates across sample sizes. The results also indicate that nonnormality, small sample size, MNAR, and multicollinearity might adversely affect convergence rate and the validity of statistical inferences concerning parameter estimates and model fit statistics. 相似文献
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Therese D. Pigott 《Educational Research and Evaluation》2013,19(4):353-383
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. 相似文献
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插秧机是现代农业向自动化方向发展过程中使用的重要工具之一,由于受到地理环境和设备等因素影响,插秧机在工作中难免会出现缺秧及漂秧等情况。传统对缺秧和漂秧的识别主要依靠经验与人工作业,效率低下、准确度不高,因此提出基于深度卷积神经网络（CNN）算法的缺秧与漂秧图像识别技术。首先计算缺秧与漂秧数据图像样本的质心位置,根据质心间距离是否在合理范围内识别缺秧,然后提取秧苗样本特征建立样本库,对采集的秧苗图像数据进行分析处理,再与样本库进行对比,以此判断插秧机在工作过程中是否存在缺秧和漂秧情况。通过对仿真算例进行测试,验证了算法的有效性,其准确率达到 90%以上。该方法对于农业自动化的发展具有重要意义,对于相关实践能起到一定的推动作用。 相似文献
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As useful multivariate techniques, structural equation models have attracted significant attention from various fields. Most existing statistical methods and software for analyzing structural equation models have been developed based on the assumption that the response variables are normally distributed. Several recently developed methods can partially address violations of this assumption, but still encounter difficulties in analyzing highly nonnormal data. Moreover, the presence of missing data is a practical issue in substantive research. Simply ignoring missing data or improperly treating nonignorable missingness as ignorable could seriously distort statistical influence results. The main objective of this article is to develop a Bayesian approach for analyzing transformation structural equation models with highly nonnormal and missing data. Different types of missingness are discussed and selected via the deviance information criterion. The empirical performance of our method is examined via simulation studies. Application to a study concerning people’s job satisfaction, home life, and work attitude is presented. 相似文献
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《Learning and individual differences》2007,17(1):25-34
This paper serves as an illustration of the usefulness of structurally incomplete designs as an approach to reduce the length of educational questionnaires. In structurally incomplete test designs, respondents only fill out a subset of the total item set, while all items are still provided to the whole sample. The scores on the unadministered items are subsequently dealt with by using methods for the estimation of missing data. Two structurally incomplete test designs — one recording two thirds, and the other recording a half of the potentially complete data — were applied to the complete item scores on 8 educational psychology scales. The incomplete item scores were estimated with missing data method Data Augmentation. Complete and estimated test data were compared at the estimates of total scores, reliability, and predictive validity of an external criterion. The reconstructed data yielded estimates that were very close to the values in the complete data. As expected the statistical uncertainty was higher in the design that recorded fewer item scores. It was concluded that the procedure of applying incomplete test designs and subsequently dealing with the missing values is very fruitful for reducing questionnaire length. 相似文献
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《Structural equation modeling》2013,20(1):128-141
Maximum likelihood algorithms for use with missing data are becoming commonplace in microcomputer packages. Specifically, 3 maximum likelihood algorithms are currently available in existing software packages: the multiple-group approach, full information maximum likelihood estimation, and the EM algorithm. Although they belong to the same family of estimator, confusion appears to exist over the differences among the 3 algorithms. This article provides a comprehensive, nontechnical overview of the 3 maximum likelihood algorithms. Multiple imputation, which is frequently used in conjunction with the EM algorithm, is also discussed. 相似文献
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When dealing with missing responses, two types of omissions can be discerned: items can be skipped or not reached by the test taker. When the occurrence of these omissions is related to the proficiency process the missingness is nonignorable. The purpose of this article is to present a tree‐based IRT framework for modeling responses and omissions jointly, taking into account that test takers as well as items can contribute to the two types of omissions. The proposed framework covers several existing models for missing responses, and many IRTree models can be estimated using standard statistical software. Further, simulated data is used to show that ignoring missing responses is less robust than often considered. Finally, as an illustration of its applicability, the IRTree approach is applied to data from the 2009 PISA reading assessment. 相似文献