Identifiablity of Models for Clusterwise Linear Regression

The model choice and the interpretation of the parameters are discussed as well as the use of the identifiability concept for fixed partition models. The concept is generalized to "partial identifiability".     

On the Identifiability in the Latent Budget Model

end-member model . A major drawback of the latent budget model is that, in general, the model is not identifiable, which complicates the interpretation of the model considerably. This paper studies the geometry and identifiability of the latent budget model. Knowledge of the geometric structure of the model is used to specify an appropriate criterion to identify the model. The results are illustrated by an empirical data set.     

Multidimensional Item Response Theory Models with Collateral Information as Poisson Regression Models

Multiple choice items on tests and Likert items on surveys are ubiquitous in educational, social and behavioral science research; however, methods for analyzing of such data can be problematic. Multidimensional item response theory models are proposed that yield structured Poisson regression models for the joint distribution of responses to items. The methodology presented here extends the approach described in Anderson, Verkuilen, and Peyton (2010) that used fully conditionally specified multinomial logistic regression models as item response functions. In this paper, covariates are added as predictors of the latent variables along with covariates as predictors of location parameters. Furthermore, the models presented here incorporate ordinal information of the response options thus allowing an empirical examination of assumptions regarding the ordering and the estimation of optimal scoring of the response options. To illustrate the methodology and flexibility of the models, data from a study on aggression in middle school (Espelage, Holt, and Henkel 2004) is analyzed. The models are fit to data using SAS.     

Mixture Model Analysis of Complex Samples

We investigate the effects of a complex sampling design on the estimation of mixture models. An approximate or pseudo likelihood approach is proposed to obtain consistent estimates of class-specific parameters when the sample arises from such a complex design. The effects of ignoring the sample design are demonstrated empirically in the context of an international value segmentation study in which a multinomial mixture model is applied to identify segment-level value rankings. The analysis reveals that ignoring the sample design results in both an incorrect number of segments as identified by information criteria and biased estimates of segment-level parameters.     

A comparison between two distance-based discriminant principles

A distance-based classification procedure suggested by Matusita (1956) has long been available as an alternative to the usual Bayes decision rule. Unsatisfactory features of both approaches when applied to multinomial data led Goldstein and Dillon (1978) to propose a new distance-based principle for classification. We subject the Goldstein/Dillon principle to some theoretical scrutiny by deriving the population classification rules appropriate not only to multinomial data but also to multivariate normal and mixed multinomial/multinormal data. These rules demonstrate equivalence of the Goldstein/Dillon and Matusita approaches for the first two data types, and similar equivalence is conjectured (but not explicitly obtained) for the mixed data case. Implications for sample-based rules are noted.     

Functional PCA and Base-Line Logit Models

In many statistical applications data are curves measured as functions of a continuous parameter as time. Despite of their functional nature and due to discrete-time observation, these type of data are usually analyzed with multivariate statistical methods that do not take into account the high correlation between observations of a single curve at nearby time points. Functional data analysis methodologies have been developed to solve these type of problems. In order to predict the class membership (multi-category response variable) associated to an observed curve (functional data), a functional generalized logit model is proposed. Base-line category logit formulations will be considered and their estimation based on basis expansions of the sample curves of the functional predictor and parameters. Functional principal component analysis will be used to get an accurate estimation of the functional parameters and to classify sample curves in the categories of the response variable. The good performance of the proposed methodology will be studied by developing an experimental study with simulated and real data.     

GENFOLD2: A set of models and algorithms for the general UnFOLDing analysis of preference/dominance data

A general set of multidimensional unfolding models and algorithms is presented to analyze preference or dominance data. This class of models termed GENFOLD2 (GENeral UnFOLDing Analysis-Version 2) allows one to perform internal or external analysis, constrained or unconstrained analysis, conditional or unconditional analysis, metric or nonmetric analysis, while providing the flexibility of specifying and/or testing a variety of different types of unfolding-type preference models mentioned in the literature including Caroll's (1972, 1980) simple, weighted, and general unfolding analysis. An alternating weighted least-squares algorithm is utilized and discussed in terms of preventing degenerate solutions in the estimation of the specified parameters. Finally, two applications of this new method are discussed concerning preference data for ten brands of pain relievers and twelve models of residential communication devices.     

Belief Systems and the Modeling Relation

The paper presents the most general aspects of scientific modeling and shows that social systems naturally include different belief systems (i.e. different models). Belief systems differ in a variety of respects, most notably in the selection of suitable qualities to encode and the internal structure of the observables. The following results emerge from the analysis: (1) conflict is explained by showing that different models encode different qualities, which implies that they model different realities; (2) explicitly connecting models to the realities that they encode makes it possible to clarify the relations among models; (3) by understanding that social systems are complex one knows that there is no chance of developing a maximal model of the system; (4) the distinction among different levels of depth implicitly includes a strategy for inducing change; (5) identity-preserving models are among the most difficult to modify; (6) since models do not customarily generate internal signals of error, strategies with which to determine when models are out of synch with their situations are especially valuable; (7) changing the form of power from a zero sum game to a positive sum game helps transform the nature of conflicts.     

A Theory of Scientific Model Construction: <Emphasis Type="Italic">The Conceptual Process of Abstraction and Concretisation</Emphasis>

The process of abstraction and concretisation is a label used for an explicative theory of scientific model-construction. In scientific theorising this process enters at various levels. We could identify two principal levels of abstraction that are useful to our understanding of theory-application. The first level is that of selecting a small number of variables and parameters abstracted from the universe of discourse and used to characterise the general laws of a theory. In classical mechanics, for example, we select position and momentum and establish a relation amongst the two variables, which we call Newton’s 2nd law. The specification of the unspecified elements of scientific laws, e.g. the force function in Newton’s 2nd law, is what would establish the link between the assertions of the theory and physical systems. In order to unravel how and with what conceptual resources scientific models are constructed, how they function and how they relate to theory, we need a view of theory-application that can accommodate our constructions of representation models. For this we need to expand our understanding of the process of abstraction to also explicate the process of specifying force functions etc. This is the second principal level at which abstraction enters in our theorising and in which I focus. In this paper, I attempt to elaborate a general analysis of the process of abstraction and concretisation involved in scientific- model construction, and argue why it provides an explication of the construction of models of the nuclear structure.     

A Proof of the Duality of the DINA Model and the DINO Model

The Deterministic Input Noisy Output “AND” gate (DINA) model and the Deterministic Input Noisy Output “OR” gate (DINO) model are two popular cognitive diagnosis models (CDMs) for educational assessment. They represent different views on how the mastery of cognitive skills and the probability of a correct item response are related. Recently, however, Liu, Xu, and Ying demonstrated that the DINO model and the DINA model share a “dual” relation. This means that one model can be expressed in terms of the other, and which of the two models is fitted to a given data set is essentially irrelevant because the results are identical. In this article, a proof of the duality of the DINA model and the DINO model is presented that is tailored to the form and parameterization of general CDMs that have become the new theoretical standard in cognitively diagnostic modeling.     

英文武器装备名的构成类型与构造模式研究

文章面向英文武器装备名的自动识别,构建了涵盖陆海空等多个军兵种武器装备名称的基础数据库,并通过人工标注方式,对英文武器装备名的构成类型与构造模式进行了较为全面的调查,总结出英文武器装备名的主要构成要素和模式。文章还利用武器装备名的测试数据集对提出的模式规则进行了实验测试,测试结果显示文章提出的模式具有较好的描写能力,能够以较高准确率识别出武器装备名称。文章的研究方法与成果不仅能够有效支持军事领域的信息处理和相关教学工作,同时也对其他语种的相关研究具有重要参考价值。     

复杂系统的隐喻描述与模型描述

由隐喻描述走向模型描述是复杂性科学成熟的标志.本文重点介绍了复杂系统的两种模型描述方法:形式化系统模型与关联论模型,并比较它们各自的优缺点.两种模型是从不同的角度对复杂系统的描述,在复杂系统的理论研究与实际运用中都有着重要的、不可替代的意义.复杂系统的未来研究方向并不是对于两种模型的"整合",而是两种模型交替并用,以多元视角科学地揭示复杂系统的本质.     

航空航天纺织结构复合材料力学性能研究进展与展望

随着航空航天事业的快速发展,纺织结构复合材料以其良好的性能成为航空航天飞行器的重要结构材料。本文从纺织结构复合材料的工程应用、细观力学分析模型、有限元方法和强度预测模型等几个方面介绍了纺织结构复合材料力学性能研究的进展情况,提出了当前研究存在的问题,并展望了今后研究的发展方向。     

Constructing optimal ultrametrics

Clique optimization (CLOPT) is a family of graph clustering procedures that construct parsimonious ultrametrics by executing a sequence of divisive and agglomerative operations. Every CLOPT procedure is associated with a distinct graph-partitioning heuristic. Seven HCS methods, a mathematical programming algorithm, and two CLOPT heuristics were evaluated on simulated data. These data were obtained by distorting ultrametric partitions and hierarchies. In general, internally optimal models yielded externally optimal models. By recovering near-optimal solutions more consistently, CLOPT2 emerged as the most robust technique.     

Adversus Singularitates: The Ontology of Space–Time Singularities

I argue that there are no physical singularities in space–time. Singular space–time models do not belong to the ontology of the world, because of a simple reason: they are concepts, defective solutions of Einstein’s field equations. I discuss the actual implication of the so-called singularity theorems. In remarking the confusion and fog that emerge from the reification of singularities I hope to contribute to a better understanding of the possibilities and limits of the theory of general relativity.     

西部开发中的技术转移与文化摩擦研究论纲

技术转移是实施西部开发的重要途径,外来技术、文化与西部技术、文化之间的差异是文化摩擦的重要成因,同时实施文化变革与技术民族化是消除文化摩擦的主要对策.对此,需要做总体陈述和专题分析.     

Function approximation for incompletely specified regression models

This report extends earlier work by Brailovsky on regression theory and methodology, giving particular emphasis to function approximation for incompletely specified models. The interest here is with situations where the form of the regression relation is not known in advance. We discuss several difficulties that arise in using local approximation and linear regression methods, and propose ways to overcome these problems. To aid the data analyst in developing a suitable model, an illustrative table is derived for determining the number of initial explanatory functions justifiable for a given prespecified confidence level. The general approach formulated here is illustrated with an application to medical data. Relevance to classification and possible extensions are discussed.     

科学知识变化的三种解释模式及其评价

科学知识的发展变化问题是20世纪科学哲学所关注的核心论题之一。纵观20世纪科学哲学的发展，主要地形成了三种解释模式：逻辑一理性论模式、认知论模式和社会学模式。虽然在20世纪的最后30年认知主义者和科学知识社会学家都对逻辑一理性论进行了猛烈的批判，但它们也未能对科学知识的变化问题给出令人满意的说明。对科学知识发展变化的完全说明应是这三种模式的有机统一。     

论科学理解的形成机制

在与科学说明的关系之比较中科学理解的概念、原理得以辨析清楚,其基本立场也在与一般理解概念的区别中得以凸显出来。其中判断力、逻辑力是形成科学理解的主要工具,类比是重要的机制,而陌生知识通过知识背景的整合从而丰富、改组原有知识结构是主要的过程。     

金属-末端元素多重键配合物的中文译名

金属-末端元素多重键配合物是一类在结构和性质上都颇为重要的化合物,但至今尚无简洁统一的中文译名。考虑到中文化学术语的音译和意译原则,建议将其译为金属X独配合物(X为配位元素名)。