Divisive Latent Class Modeling as a Density Estimation Method for Categorical Data

Traditionally latent class (LC) analysis is used by applied researchers as a tool for identifying substantively meaningful clusters. More recently, LC models have also been used as a density estimation tool for categorical variables. We introduce a divisive LC (DLC) model as a density estimation tool that may offer several advantages in comparison to a standard LC model. When using an LC model for density estimation, a considerable number of increasingly large LC models may have to be estimated before sufficient model-fit is achieved. A DLC model consists of a sequence of small LC models. Therefore, a DLC model can be estimated much faster and can easily utilize multiple processor cores, meaning that this model is more widely applicable and practical. In this study we describe the algorithm of fitting a DLC model, and discuss the various settings that indirectly influence the precision of a DLC model as a density estimation tool. These settings are illustrated using a synthetic data example, and the best performing algorithm is applied to a real-data example. The generated data example showed that, using specific decision rules, a DLC model is able to correctly model complex associations amongst categorical variables.     

Power and Sample Size Computation for Wald Tests in Latent Class Models

Latent class (LC) analysis is used by social, behavioral, and medical science researchers among others as a tool for clustering (or unsupervised classification) with categorical response variables, for analyzing the agreement between multiple raters, for evaluating the sensitivity and specificity of diagnostic tests in the absence of a gold standard, and for modeling heterogeneity in developmental trajectories. Despite the increased popularity of LC analysis, little is known about statistical power and required sample size in LC modeling. This paper shows how to perform power and sample size computations in LC models using Wald tests for the parameters describing association between the categorical latent variable and the response variables. Moreover, the design factors affecting the statistical power of these Wald tests are studied. More specifically, we show how design factors which are specific for LC analysis, such as the number of classes, the class proportions, and the number of response variables, affect the information matrix. The proposed power computation approach is illustrated using realistic scenarios for the design factors. A simulation study conducted to assess the performance of the proposed power analysis procedure shows that it performs well in all situations one may encounter in practice.     

Model-based Clustering with Noise: Bayesian Inference and Estimation

This paper proposes a new way of overcoming the existing limitations. It generalizes the model used in the previous approaches by introducing a more comprehensive portfolio of covariance matrix structures. Further, this paper proposes a Bayesian solution in the presence of the noise in clustering problems. The performace of the proposed method is first studied by simulation; the procedure is also applied to the analysis of data concerning species of butterflies and diabetes patients.     

Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering

Normal mixture models are widely used for statistical modeling of data, including cluster analysis. However maximum likelihood estimation (MLE) for normal mixtures using the EM algorithm may fail as the result of singularities or degeneracies. To avoid this, we propose replacing the MLE by a maximum a posteriori (MAP) estimator, also found by the EM algorithm. For choosing the number of components and the model parameterization, we propose a modified version of BIC, where the likelihood is evaluated at the MAP instead of the MLE. We use a highly dispersed proper conjugate prior, containing a small fraction of one observation's worth of information. The resulting method avoids degeneracies and singularities, but when these are not present it gives similar results to the standard method using MLE, EM and BIC.     

Maximum Likelihood Estimation and Model Comparison for Mixtures of Structural Equation Models with Ignorable Missing Data

The objective of this paper is to develop the maximum likelihood approach for analyzing a finite mixture of structural equation models with missing data that are missing at random. A Monte Carlo EM algorithm is proposed for obtaining the maximum likelihood estimates. A well-known statistic in model comparison, namely the Bayesian Information Criterion (BIC), is used for model comparison. With the presence of missing data, the computation of the observed-data likelihood function value involved in the BIC is not straightforward. A procedure based on path sampling is developed to compute this function value. It is shown by means of simulation studies that ignoring the incomplete data with missing entries gives less accurate ML estimates. An illustrative real example is also presented.     

大规模成矿作用与大型矿集区预测研究

《大规模成矿作用与大型矿集区预测》是国家重点基础研究发展计划 (973计划 )实施以来第一个以固体矿产资源为目标的研究项目。通过 5年 (1 999年 1 0月— 2 0 0 4年 9月 )研究 ,在多项基础地质和矿产资源成矿理论研究方面取得了重要进展 :初步提出了中国中新生代大陆成矿理论体系 ,为预测大矿和大型矿集区奠定了理论基础 ;研制和发展了 4项找矿新技术方法 ,以及提出了两种找矿新思路 ;并在实验阶段圈定了 5个矿集区尺度的找矿靶区 ,发现了一批矿化异常区。此外 ,在研究过程中还形成了 3个国家级优秀科研群体和 3个部门级优秀科研群体 ,培养出 9名优秀中青年人才以及大批博士后工作人员、博士研究生和硕士研究生 ,其中有不少中青年科学家已在国际学术组织任职。研究期间共发表科学论文 772篇 ,其中SCI检索论文2 2 7篇 (国外论文 1 2 5篇 )     

军语概念体系的框架编制原则

军语概念体系的框架编制是军语编纂工作中的一个重要基础环节,应主要把握科学性、实用性、特色性、创新性、规范性的原则,以确保框架分类合理、特色鲜明、操作便利。     

中国加入世贸组织与国民整体素质结构的调整

中国加入世贸组织后,ＷＴＯ所具有的巨大推动必将推动中国社会主义市场经济的迅速发展,促进经济结构进行大规模的调整,加快市场化进程,以更好地参与世界经济竞争。一方面通过经济竞争促使中国经济增攻进一步加快,给近几年低迷徘徊的就业市场带来亮点,给广大国民就业和素质结构调整带来新的机遇;另一方面,由于我国社会主义市场经济的发展,引进竞争机制,实行优胜劣汰,以及加入ＷＴＯ后产业结构必将进行大规模调整的双重因素     

《大衍历》日躔表的数学结构及其内插法

《大衍历》日躔表是一份四次差分相等的数表,反映一行对太阳运动复杂性的深刻认识。一行调整插值引数为不等间距型,并认为具有降阶作用,因而能够采用二次函数完成四次差分表的插值计算。但一行对插值间距只利用差分方程作了第一次逼近,没有进行迭代计算,从而影响了计算精度。     

李兰秤漏的一种可能结构及其平均流速稳定原理

秤漏是一种特殊类型的漏刻,在隋唐及北宋前期曾是司天机构的主要计时仪器。根据古文献记述,李兰秤漏很可能是使用弹簧联结权器和秤钩,以达到平均流速稳定的目的。     

人神交通的舞台——传统纳西族的创世神话及宇宙结构分析

通过对纳西族经典创世神话的阐释和分析，介绍了传统纳西族神话中的宇宙结构及其对日月运行等天文现象的解释，并将之与盖天说进行比较，同时阐明，这种宇宙观所描述的不仅是宇宙的结构，也是纳西族传统社会的结构。最后，根据传统纳西族的王权特征，论述纳西族的祭司——东巴为什么不具有观天的职能。     

Dealing with Distances and Transformations for Fuzzy C-Means Clustering of Compositional Data

Clustering techniques are based upon a dissimilarity or distance measure between objects and clusters. This paper focuses on the simplex space, whose elements??compositions??are subject to non-negativity and constant-sum constraints. Any data analysis involving compositions should fulfill two main principles: scale invariance and subcompositional coherence. Among fuzzy clustering methods, the FCM algorithm is broadly applied in a variety of fields, but it is not well-behaved when dealing with compositions. Here, the adequacy of different dissimilarities in the simplex, together with the behavior of the common log-ratio transformations, is discussed in the basis of compositional principles. As a result, a well-founded strategy for FCM clustering of compositions is suggested. Theoretical findings are accompanied by numerical evidence, and a detailed account of our proposal is provided. Finally, a case study is illustrated using a nutritional data set known in the clustering literature.     

如何驱动企业生态创新?——基于制度理论与利益相关者理论的个案研究

本文以位于浙江省杭州市的一家铅酸电池制造企业为案例,抓住制度压力来源主体,识别影响企业生态创新的关键利益相关者以及他们影响企业生态创新的内在机制,主要结论如下:第一,政府环保导向、客户环保导向、竞争者环保导向、高管环保意识是影响企业生态创新四大主要因素,影响着生态创新的不同维度;第二,政府环保导向、客户环保导向影响企业生态创新的内在机制主要包括规制、规范惩罚以及资源利诱,竞争者环保导向影响企业生态创新的内在机制主要是规制、规范合法性竞赛以及资源争夺竞赛,高管环保意识影响企业生态创新的内在机制则包括道德和利益驱动两个方面。     

我国基础研究人才评价与待遇匹配问题与对策

基础研究成果只承认世界第一,因而评价的标准较高,但现阶段我国基础研究人员待遇不仅远低于发达国家,而且明显低于从事其他研发活动的人员。为促进基础研究人才健康成长,我国一些大学采取了一些新举措,力求做到评价与待遇匹配。国家相关部门应支持这些大学的举措,并采取进一步的政策措施。     

113号、115号、117号、118号元素的命名及中文定名建议

简述了113号、115号、117号、118号元素合成和确认的过程,以及IUPAC关于新元素的命名指南。介绍了113号、115号、117号、118号元素的英文名、符号及其推荐理由,并根据新元素的中文定名原则,提出了4个新元素中文命名的草案。