A semi-parametric approach to robust parameter design

Parameter design or robust parameter design (RPD) is an engineering methodology intended as a cost-effective approach for improving the quality of products and processes. The goal of parameter design is to choose the levels of the control variables that optimize a defined quality characteristic. An essential component of RPD involves the assumption of well estimated models for the process mean and variance. Traditionally, the modeling of the mean and variance has been done parametrically. It is often the case, particularly when modeling the variance, that nonparametric techniques are more appropriate due to the nature of the curvature in the underlying function. Most response surface experiments involve sparse data. In sparse data situations with unusual curvature in the underlying function, nonparametric techniques often result in estimates with problematic variation whereas their parametric counterparts may result in estimates with problematic bias. We propose the use of semi-parametric modeling within the robust design setting, combining parametric and nonparametric functions to improve the quality of both mean and variance model estimation. The proposed method will be illustrated with an example and simulations.     

葬礼:面对死亡的社会安排 ———城市环境下葬礼仪式浅析

通过实地观察和对殡仪馆工作人员的访谈，描绘了天津市城市内部现有的国家规范框架下葬礼仪式的具体过程以及附着在这个框架下的民间习俗的现状，试图通过对国家规范框架下的“正式”葬礼仪式及与其相对的“非正式”仪式——家庭告别仪式的程序形式的简略描述，展现在城市中普通个人的葬礼基于公共环境和家庭环境不同场景所采用的不同仪式过程。这是民间习俗对规范仪式的补充，同时也是“正式”规范对这种“非正式”实践的通融和消化。在描述现状之外，作者试图讨论蕴含在葬礼仪式中，不同死亡个体的葬礼仪式形式、规模及参与者等方面的区分所展现出的一种社会对死亡个体生前所处社会结构地位的认同，这种认同不仅反映了仪式所处社会背景的秩序和结构，也强化了这个社会的秩序和价值判断标准。     

左思风力之成因初探

西晋诗人左思的诗风在西晋文坛上独树一帜，称之为“左思风力”，前人对此有较多地论述，然而就诗风形成的原因却缺乏深入地探究。本文试图从社会时代背景、家庭因素、性格特征三个方面对此加以分析，旨在能够更好地把握左思其人及其诗风。     

Estimating household structure in ancient China by using historical data: a latent class analysis of partially missing patterns

Summary.  Social data often contain missing information. The problem is inevitably severe when analysing historical data. Conventionally, researchers analyse complete records only. Listwise deletion not only reduces the effective sample size but also may result in biased estimation, depending on the missingness mechanism. We analyse household types by using population registers from ancient China (618–907 AD) by comparing a simple classification, a latent class model of the complete data and a latent class model of the complete and partially missing data assuming four types of ignorable and non-ignorable missingness mechanisms. The findings show that either a frequency classification or a latent class analysis using the complete records only yielded biased estimates and incorrect conclusions in the presence of partially missing data of a non-ignorable mechanism. Although simply assuming ignorable or non-ignorable missing data produced consistently similarly higher estimates of the proportion of complex households, a specification of the relationship between the latent variable and the degree of missingness by a row effect uniform association model helped to capture the missingness mechanism better and improved the model fit.     

Bayesian networks for imputation

Summary.  Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables ( statistical consistency ) and preservation of logical constraints in data ( logical consistency ). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census.     

Fast retraction of evidence in a probabilistic expert system

We present and justify a propagation algorithm to facilitate the simultaneous calculation, for every node in a probabilistic exper system of the distribution of the associated random quantity, conditional on all the evidence obtained about the remaining nodes.     

国外大学办学特色形成的因素分析及启示

办学特色是高等院校在这个激烈竞争的时代求得生存和发展的重要因素。国外著名大学办学特色的形成受各种因素影响。其中办学理念是核心基础,学科和专业设置是集中体现,教师和毕业生的高质量是关键支撑,而大学校长则发挥着主导作用。我国的高等院校要形成自身的办学特色,应立足于这些因素,同时结合本国国情和高校自身的实际情况,借鉴和创造并举,即以前瞻性的办学理念为指导,以建立特色学科与特色专业为方向,深化管理体制改革,扩大办学自主权,大力加强教师队伍建设。     

关于物流的科学分类问题（续）

本文最新提出物流最基本的四个固有属性：物（Material）的根本属性，流（Flow）的浪本属性，物和流的主体（Party）属性，物和流的地域（Region）属性。无论自然界物流，还是社会界物流，经济界物流，都具有这四个方面属性（RPMF）。同时，本文首次提出物流四要素RPMF原理。根据物流四要素RPMF原理，提出按各要素的不同属性对物流进行科学分类的方法，并给出物流分类图。     

Factor analysis for ranked data with application to a job selection attitude survey

Summary.  Factor analysis is a powerful tool to identify the common characteristics among a set of variables that are measured on a continuous scale. In the context of factor analysis for non-continuous-type data, most applications are restricted to item response data only. We extend the factor model to accommodate ranked data. The Monte Carlo expectation–maximization algorithm is used for parameter estimation at which the E-step is implemented via the Gibbs sampler. An analysis based on both complete and incomplete ranked data (e.g. rank the top q out of k items) is considered. Estimation of the factor scores is also discussed. The method proposed is applied to analyse a set of incomplete ranked data that were obtained from a survey that was carried out in GuangZhou, a major city in mainland China, to investigate the factors affecting people's attitude towards choosing jobs.     

冗余度机器人的运动规划碰撞算法

在考虑了操作机的关节极限、自碰撞和静态障碍物的情况下,从给定的初始位形出发,发现一条到末端效应器目标位置和姿态的相连可到达路径。方法给出了机器人操作机点到点逆运动学问题求解算法,利用碰撞算法实现了冗余度机器人运动规划,仿真验证了该方法的有效性,并表明了该方法具有较大的实用价值。