Constrained Multilevel Latent Class Models for the Analysis of Three-Way Three-Mode Binary Data

Probabilistic feature models (PFMs) can be used to explain binary rater judgements about the associations between two types of elements (e.g., objects and attributes) on the basis of binary latent features. In particular, to explain observed object-attribute associations PFMs assume that respondents classify both objects and attributes with respect to a, usually small, number of binary latent features, and that the observed object-attribute association is derived as a specific mapping of these classifications. Standard PFMs assume that the object-attribute association probability is the same according to all respondents, and that all observations are statistically independent. As both assumptions may be unrealistic, a multilevel latent class extension of PFMs is proposed which allows objects and/or attribute parameters to be different across latent rater classes, and which allows to model dependencies between associations with a common object (attribute) by assuming that the link between features and objects (attributes) is fixed across judgements. Formal relationships with existing multilevel latent class models for binary three-way data are described. As an illustration, the models are used to study rater differences in product perception and to investigate individual differences in the situational determinants of anger-related behavior.     

A Property of the CHAID Partitioning Method for Dichotomous Randomized Response Data and Categorical Predictors

In this paper, we present empirical and theoretical results on classification trees for randomized response data. We considered a dichotomous sensitive response variable with the true status intentionally misclassified by the respondents using rules prescribed by a randomized response method. We assumed that classification trees are grown using the Pearson chi-square test as a splitting criterion, and that the randomized response data are analyzed using classification trees as if they were not perturbed. We proved that classification trees analyzing observed randomized response data and estimated true data have a one-to-one correspondence in terms of ranking the splitting variables. This is illustrated using two real data sets.     

Estimation and Prediction for Stochastic Blockmodels for Graphs with Latent Block Structure

a posteriori blockmodeling for graphs is proposed. The model assumes that the vertices of the graph are partitioned into two unknown blocks and that the probability of an edge between two vertices depends only on the blocks to which they belong. Statistical procedures are derived for estimating the probabilities of edges and for predicting the block structure from observations of the edge pattern only. ML estimators can be computed using the EM algorithm, but this strategy is practical only for small graphs. A Bayesian estimator, based on the Gibbs sampling, is proposed. This estimator is practical also for large graphs. When ML estimators are used, the block structure can be predicted based on predictive likelihood. When Gibbs sampling is used, the block structure can be predicted from posterior predictive probabilities. A side result is that when the number of vertices tends to infinity while the probabilities remain constant, the block structure can be recovered correctly with probability tending to 1.     

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.     

作为方法的空间

传统的空间研究,倾向于将空间视为一种"对象"。这种观念源自传统认识论对于空间属性的确认,即把空间看作绝对的、静止的几何学概念,或是某种先天纯粹的知觉形式,由此造成了空间阐释张力的严重不足。在列斐伏尔和福柯的空间思想引领之下,学界出现了"空间转向"之热潮,改变了研究者们对于空间的既往认识,其不再只是被动的研究对象,还可作为一种方法,成为认识问题的媒介与方式。这种空间方法论的创变,为研究界带来了极富革命性的启思。但就目前来说,空间方法论仍存有不少局限,这需要引起研究者们的及时关注。     

作为方法的对称和非对称

本文讨论了对称和非对称概念和界定，关系，重点研究了对称变换在现代科学技术中的应用，指出了对称和非对称方法的普遍意义和学术价值。     

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.     

A Generalized Rand-Index Method for Consensus Clustering of Separate Partitions of the Same Data Base

T clusters, based on J distinct, contributory partitions (or, equivalently, J polytomous attributes). We describe a new model/algorithm for implementing this objective. The method's objective function incorporates a modified Rand measure, both in initial cluster selection and in subsequent refinement of the starting partition. The method is applied to both synthetic and real data. The performance of the proposed model is compared to latent class analysis of the same data set.     

以职业为棱镜研究技术

工程伦理学作为技术伦理和职业伦理的一个分支学科,聚焦于研究以一定方法开发技术的某些人员如工程师,而不是研究程序、产品或系统的开发。对于技术研究者来说,尝试通过职业伦理、特别是工程伦理提供的资源来研究技术不仅是有趣的、而且是有帮助的。文章主要包括四个部分:1)对职业概念进行一个系统概述;2)评论技术伦理与职业的关系;3)具体分析通过职业棱镜研究技术的优势;以及4)作为一个研究者,如何使用职业作为棱镜研究技术以及它的伦理问题,并提出一些建议。     

The median procedure for n-trees as a maximum likelihood method

A few axioms are presented which allow the median procedure for n-trees to be given a maximum likelihood interpretation.Research supported by grant number N00014-89-J-1643 from the Office of Naval Research. The author would like to thank the referees for their helpful comments.     

作为人工智能自我知识匹配模型的阐释性感觉理论

人工智能的兴起,激发了哲学家将人工智能与人类心智进行全方位的比较,但是鲜有学者思考过,一个人工智能系统,是否能够"认识它自己"。诊断这项阙漏的根源,为此提供正当性理由便是必要的。基于当下人工智能的发展水平和技术前景,表明人工智能原则上可以拥有关于自身信念、欲望和意图之类的命题态度的自我知识。从既有的关于人类自我知识的主流理论中,筛选出一个与人工智能相匹配的理论模型。该模型类似于卡鲁瑟斯的阐释性感觉理论,它也体现了卡萨姆所说的"实质性自我知识"的优势。     

Complete linkage as a multiple stopping rule for single linkage clustering

Two commonly used clustering criteria are single linkage, which maximizes the minimum distance between clusters, and complete linkage, which minimizes the maximum distance within a cluster. By synthesizing these criteria, partitions of objects are sought which maximize a combined measure of the minimum distance between clusters and the maximum distance within a cluster. Each combined measure is shown to select a partition in the single linkage hierarchy. Therefore, in effect, complete linkage is used to provide a stopping rule for single linkage. An algorithm is outlined which uses the distance between each pair of objects twice only. To illustrate the method, an example is given using 23 Glamorganshire soil profiles.     

The Factorial Minimum Spanning Tree as a Reference for a Synthetic Index of Complex Phenomena

This paper is aimed at combining both the properties of factorial subspaces and those of the Minimum Spanning Tree Algorithm (MST) to obtain a reference structure (the Maximum Path) in which the statistical units are in reduced subordering. The coordinates (factor scores) of the statistical units in a multi-factorial subspace through Principal Component Analysis are the basis for the Minimum Spanning Tree. In the MST, we single out a path of maximum length. On the Maximum Path each graduation obtained for the unit can be used as a Synthetic Index of the phenomenon analyzed. Two distinct strategies lead to the choice of the subspace in which we have the best representation of the units in the Maximum Path. The validity of the method is confirmed by results achieved in various applications to real data.     

重复元件介导的重组是新嵌合基因形成的一个重要机制

先前的研究提示重复元件在产生新的嵌合基因中具有机制性的作用,这与经典的外显子洗牌模型是一致的,它依赖于非同源重组。而近期一些关于染色体异常的研究结果显示,异位的同源重组可能对于产生新基因也非常重要。本项研究通过对8种果蝇的年轻基因进行筛选和分析,从中识别出了17个重复片段,它们是在最近1200万年中通过异位重组形成的。它们中的大多数已具有功能,并进化出多样的表达类型和嵌合结构。这些结果证实,重复元件介导的非等位同源重组在产生新嵌合基因中起到重要作用。     

组蛋白去乙酰化酶SIRT1:血管性疾病新的潜在治疗靶点

老化是血管性疾病的主要危险因素之一.在老化过程中,内皮功能失调、基质金属蛋白酶调控失常、炎症分子表达增加、氧化应激增加和端粒长度缩短等因素引起了管腔扩张、内膜中层厚度改变等血管结构和僵硬度增加等功能的改变.这些改变使得冠状动脉粥样硬化性心脏病、原发性高血压、脑卒中等与年龄相关的血管性疾病易于发生.因此,抑制年龄相关的分子表达可能是干预血管性疾病的有效途径.哺乳动物SIRT1是一种NAD+依赖的组蛋白去乙酰化酶.近年来,对于SIRT1在血管性疾病中的作用受到广泛关注.本文结合我们的研究,阐述了SIRT1在血管老化及相关疾病中的作用,并提出SIRT1可能成为治疗动脉粥样硬化等年龄相关血管性疾病的潜在靶点.